- The A.V. Club
- The Takeout
- The Inventory
Here’s how Twitter is describing itself ahead of the IPO
Below is the section of Twitter’s IPO filing in which it describes the nature of its business and some top-line statistics about the company. You can annotate any paragraph by hovering over the text and clicking the bubble that appears to the right.
The photo above is by Scott Beale/Laughing Squid and used under a Creative Commons license.
This summary highlights selected information that is presented in greater detail elsewhere in this prospectus. This summary does not contain all of the information you should consider before investing in our common stock. You should read this entire prospectus carefully, including the sections titled “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations” and our consolidated financial statements and the related notes included elsewhere in this prospectus, before making an investment decision. Unless the context otherwise requires, the terms “Twitter,” “the company,” “we,” “us” and “our” in this prospectus refer to Twitter, Inc. and its consolidated subsidiaries.
Twitter is a global platform for public self-expression and conversation in real time. By developing a fundamentally new way for people to create, distribute and discover content, we have democratized content creation and distribution, enabling any voice to echo around the world instantly and unfiltered.
Our platform is unique in its simplicity: Tweets are limited to 140 characters of text. This constraint makes it easy for anyone to quickly create, distribute and discover content that is consistent across our platform and optimized for mobile devices. As a result, Tweets drive a high velocity of information exchange that makes Twitter uniquely “live.” We aim to become an indispensable daily companion to live human experiences.
People are at the heart of Twitter. We have already achieved significant global scale, and we continue to grow. We have more than 215 million monthly active users, or MAUs, and more than 100 million daily active users, spanning nearly every country. Our users include millions of people from around the world, as well as influential individuals and organizations, such as world leaders, government officials, celebrities, athletes, journalists, sports teams, media outlets and brands. Our users create approximately 500 million Tweets every day.
Twitter is a public, real-time platform where any user can create a Tweet and any user can follow other users. We do not impose restrictions on whom a user can follow, which greatly enhances the breadth and depth of available content and allows users to discover the content they care about most. Additionally, users can be followed by thousands or millions of other users without requiring a reciprocal relationship, enhancing the ability of our users to reach a broad audience. The public nature of our platform allows us and others to extend the reach of Twitter content beyond our properties. Media outlets distribute Tweets beyond our properties to complement their content by making it more timely, relevant and comprehensive. Tweets have appeared on over one million third-party websites, and in the second quarter of 2013 there were approximately 30 billion online impressions of Tweets off of our properties.
Twitter provides a compelling and efficient way for people to stay informed about their interests, discover what is happening in their world right now and interact directly with each other. We enable the timely creation and distribution of ideas and information among people and organizations at a local and global scale. Our platform allows users to browse through Tweets quickly and explore content more deeply through links, photos, media and other applications that can be attached to each Tweet. As a result, when events happen in the world, whether planned, like sporting events and television shows, or unplanned, like natural disasters and political revolutions, the digital experience of those events happens in real time on Twitter. People can communicate with each other during these events as they occur, creating powerful shared experiences.
We are inspired by how Twitter has been used around the world. President Obama used our platform to first declare victory publicly in the 2012 U.S. presidential election, with a Tweet that was viewed approximately 25 million times on our platform and widely distributed offline in print and broadcast media. A local resident in Abbottabad, Pakistan unknowingly reported the raid on Osama Bin Laden’s compound on Twitter hours before traditional media and news outlets began to report on the event. During the earthquake and subsequent tsunami in Japan, people came to Twitter to understand the extent of the disaster, find loved ones and follow the nuclear crisis that ensued. For individuals and organizations seeking timely distribution of content, Twitter moves beyond traditional broadcast mediums by assembling connected audiences. Twitter brings people together in shared experiences allowing them to discover and consume content and just as easily add their own voice in the moment.
Our platform partners and advertisers enhance the value we create for our users.
Millions of platform partners, which include publishers, media outlets and developers, have integrated with Twitter, adding value to our user experience by contributing content to our platform, broadly distributing content from our platform across their properties and using Twitter content and tools to enhance their websites and applications. Many of the world’s most trusted media outlets, including the BBC, CNN and Times of India, regularly use Twitter as a platform for content distribution.
Advertisers use our Promoted Products, the majority of which are pay-for-performance, to promote their brands, products and services, amplify their visibility and reach, and complement and extend the conversation around their advertising campaigns. We enable our advertisers to target an audience based on a variety of factors, including a user’s Interest Graph. The Interest Graph maps, among other things, interests based on users followed and actions taken on our platform, such as Tweets created and engagement with Tweets. We believe a user’s Interest Graph produces a clear and real-time signal of a user’s interests, greatly enhancing the relevance of the ads we can display for users and enhancing our targeting capabilities for advertiser
Although we do not generate revenue directly from users or platform partners, we benefit from network effects where more activity on Twitter results in the creation and distribution of more content, which attracts more users, platform partners and advertisers, resulting in a virtuous cycle of value creation.
Mobile has become the primary driver of our business. Our mobile products are critical to the value we create for our users, and they enable our users to create, distribute and discover content in the moment and on-the-go. The 140 character constraint of a Tweet emanates from our origins as an SMS-based messaging system, and we leverage this simplicity to develop products that seamlessly bridge our user experience across all devices. In the three months ended June 30, 2013, 75% of our average MAUs accessed Twitter from a mobile device, including mobile phones and tablets, and over 65% of our advertising revenue was generated from mobile devices. We expect that the proportion of active users on, and advertising revenue generated from, mobile devices, will continue to grow in the near term.
We have experienced rapid growth in our revenue in recent periods. From 2011 to 2012, revenue increased by 198% to $316.9 million, net loss decreased by 38% to $79.4 million and Adjusted EBITDA increased by 149% to $21.2 million. From the six months ended June 30, 2012 to the six months ended June 30, 2013, revenue increased by 107% to $253.6 million, net loss increased by 41% to $69.3 million and Adjusted EBITDA increased by $20.7 million to $21.4 million. For information on how we define and calculate Adjusted EBITDA, and a reconciliation of net loss to Adjusted EBITDA, see the section titled “—Summary Consolidated Financial and Other Data—Non-GAAP Financial Measures.”
We have also experienced significant growth in our user base, as measured by MAUs, and user engagement, as measured by timeline views.
For information on how we define and calculate the number of MAUs and the number of timeline views and factors that can affect these metrics, see the sections titled “Management’s Discussion and Analysis of Financial Condition and Results of Operations—Key Metrics” and “Industry Data and Company Metrics.”
The Evolution of Content Creation, Distribution and Discovery
The Internet and digitization have allowed for virtually all content to be made available online, but the vast array of content has made it difficult for people to find what is important or relevant to them. Over time, technologies have been developed to address this challenge:
In the early to mid-1990s, browsers, including Netscape Navigator and Internet Explorer, presented content on the Internet in a visually appealing manner and allowed people to navigate to specific websites, but the content experience was generally not personalized or tailored to a person’s interests and information was often difficult to find.
In the mid to late-1990s, Yahoo!, AOL, MSN and other web portals aggregated and categorized popular content and other communication features to help people discover relevant information on the Internet. These portals, while convenient, and with some ability to personalize, offer access to a limited amount of content.
In the early-2000s, Google and other search engines began providing a way to search a vast amount of content, but search results are limited by the quality of the search algorithm and the amount of content in the search index. In addition, given the lag between live events and the creation and indexing of digital content, search engine results may lack real-time information. Also, search engines generally do not surface content that a person has not requested, but may find interesting.
In the mid-2000s, social networks, such as Facebook, emerged as a new way to connect with friends and family online, but they are generally closed, private networks that do not include content from outside a person’s friends, family and mutual connections. Consequently, the depth and breadth of content available to people is generally limited. Additionally, content from most social networks is not broadly available off their networks, such as on other websites, applications or traditional media outlets like television, radio and print.
Twitter Continues the Evolution
Twitter continues the evolution of content creation, distribution and discovery by combining the following four elements at scale to create a global platform for public self-expression and conversation in real time. We believe Twitter can be the content creation, distribution and discovery platform for the Internet and evolving mobile ecosystem.
Twitter is open to the world. Content on Twitter is broadly accessible to our users and unregistered visitors. All users can create Tweets and follow other users. In addition, because the public nature of Twitter allows content to travel virally on and off our properties to other websites and media, such as television and print, people can benefit from Twitter content even if they are not Twitter users or following the user that originally tweeted.
News breaks on Twitter. The combination of our tools, technology and format enables our users to quickly create and distribute content globally in real time with 140 keystrokes or the flash of a photo, and the click of a button. The ease with which our users can create content combined with our broad reach results in users often receiving content faster than other forms of media.
Twitter is where users come to express themselves and interact with the world. Our users can interact on Twitter directly with other users, including people from around the world, as well as influential individuals and organizations. Importantly, these interactions can occur in public view, thereby creating an opportunity for all users to follow and participate in conversations on Twitter.
Tweets go everywhere. The simple format of a Tweet, the public nature of content on Twitter and the ease of distribution off our properties allow media outlets to display Tweets on their online and offline properties, thereby extending the reach of Tweets beyond our properties. A 2013 study conducted by Arbitron Inc. and Edison Research found that 44% of Americans hear about Tweets through media channels other than Twitter almost every day.
Our Value Proposition to Users
People are at the heart of Twitter. We have more than 215 million MAUs from around the world. People come to Twitter for many reasons, and we believe that two of the most significant are the breadth of Twitter content and our broad reach. Our users consume content and engage in conversations that interest them by discovering and following the people and organizations they find most compelling.
Our platform provides our users with the following benefits:
Sharing Content with the World
Users leverage our platform to express themselves publicly to the world, share with their friends and family and participate in conversations. The public, real-time nature and tremendous global reach of our platform make it the content distribution platform of choice for many of the world’s most influential individuals and organizations, as well as millions of people and small businesses.
Discovering Unique and Relevant Content
Twitter’s over 215 million MAUs, spanning nearly every country, provide great breadth and depth of content across a broad range of topics, including literature, politics, finance, music, movies, comedy, sports and news.
Breaking News and Engaging in Live Events
Users come to Twitter to discover what is happening in the world right now directly from other Twitter users. On Twitter, users tweet about live events instantly, whether it is celebrities tweeting to their fans, journalists breaking news or people providing eyewitness accounts of events as they unfold. Many individuals and organizations choose to break news first on Twitter because of the unique reach and speed of distribution on our platform. As a result, Twitter is a primary source of information and complements traditional media as a second screen, enhancing the overall experience of an event by allowing users to share the experience with other users in real time. We believe this makes Twitter the social soundtrack to life in the moment.
Participating in Conversations
Through Twitter, users not only communicate with friends and family, but they also participate in conversations with other people from around the world, in ways that would not otherwise be possible. In addition to participating in conversations, users can simply follow conversations on Twitter or express interest in the conversation by retweeting or favoriting.
Our Value Proposition to Platform Partners
The value we create for our users is enhanced by our platform partners, which include publishers, media outlets and developers. These platform partners have integrated with Twitter through an application programming interface, or API, that we provide which allows them to contribute their content to our platform, distribute Twitter content across their properties and use Twitter content and tools to enhance their websites and applications. We provide a set of development tools, APIs and embeddable widgets that allow our partners to seamlessly integrate with our platform.
We provide our platform partners with the following benefits:
Platform partners use Twitter as a complementary distribution channel to expand their reach and engage with their audiences. Publishers and media outlets contribute content created for other media channels to Twitter and tweet content specifically created for Twitter. We provide platform partners with a set of widgets that they can embed on their websites and an API for their mobile applications to enable Twitter users to tweet content directly from those properties. As our users engage with this content on Twitter, they can be directed back to our partners’ websites and applications.
Complementary Real-Time and Relevant Content
Twitter enables platform partners to embed or display relevant Tweets on their online and offline properties to enhance the experience for their users. Additionally, by enhancing the activity related to their programming or event on Twitter, media outlets can drive tune-in and awareness of their original content, leveraging Twitter’s strength as a second screen for television programming. For example, during Super Bowl XLVII, over 24 million Tweets regarding the Super Bowl were sent during the game alone and 45% of television ads shown during the Super Bowl used a hashtag to invite viewers to engage in conversation about those television ads on Twitter .
Canvas for Enhanced Content with Twitter Cards
Platform partners use Twitter Cards to embed images, video and interactive content directly into a Tweet. Twitter Cards allow platform partners to create richer content that all users can interact with and distribute.
Building with Twitter Content
Platform partners leverage Tweets to enhance the experience for their users. Developers incorporate Twitter content and use Twitter tools to build a broad range of applications. Media partners incorporate Twitter content to enrich their programming and increase viewer engagement by providing real-time Tweets that express public opinion and incorporate results from viewer polls on Twitter.
Our Value Proposition to Advertisers
We provide compelling value to our advertisers by delivering the ability to reach a large global audience through our unique set of advertising services, the ability to target ads based on our deep understanding of our users and the opportunity to generate significant earned media. Advertisers can use Twitter to communicate directly with their followers for free, but many choose to purchase our advertising services to reach a broader audience and further promote their brands, products and services.
Our platform provides our advertisers with the following benefits:
Unique Ad Formats Native to the User Experience
Our Promoted Products, which are Promoted Tweets, Promoted Accounts and Promoted Trends, provide advertisers with an opportunity to reach our users without disrupting or detracting from the user experience on our platform.
Our pay-for-performance Promoted Products enable advertisers to reach users based on many factors. Importantly, because our asymmetric follow model does not require mutual follower relationships, people can follow the users that they find most interesting. These follow relationships are then combined with other factors, such as the actions that users take on our platform, including the Tweets they engage with and what they tweet about, to form a user’s Interest Graph. We believe a user’s Interest Graph produces a clear and real-time signal of a user’s interests, greatly enhancing our targeting capability.
Earned Media and Viral Global Reach
The public and widely distributed nature of our platform enables Tweets to spread virally, potentially reaching all of our users and people around the world. Our users retweet, reply to or start conversations about interesting Tweets, whether those Tweets are Promoted Tweets or organic Tweets by advertisers. An advertiser only gets charged when a user engages with a Promoted Tweet that was placed in a user’s timeline because of its promotion. By creating highly compelling and engaging ads, our advertisers can benefit from users retweeting their content across our platform at no incremental cost.
Advertising in the Moment
Twitter’s real-time nature allows our advertisers to capitalize on live events, existing conversations and trending topics. By using our Promoted Products, advertisers can create a relevant ad in real time that is shaped by these events, conversations and topics.
Pay-for-Performance and Attractive Return on Investment
Our advertisers pay for Promoted Tweets and Promoted Accounts on a pay-for-performance basis. Our advertisers only pay us when a user engages with their ad, such as when a user clicks on a link in a Promoted Tweet, expands a Promoted Tweet, replies to or favorites a Promoted Tweet, retweets a Promoted Tweet for the first time, follows a Promoted Account or follows the account that tweets a Promoted Tweet. The pay-for-performance structure aligns our interests in delivering relevant and engaging ads to our users with those of our advertisers.
Extension of Offline Advertising Campaigns
Twitter advertising complements offline advertising campaigns, such as television ads. Integrating hashtags allows advertisers to extend the reach of an offline ad by driving significant earned media and continued conversation on Twitter.
Our Value Proposition to Data Partners
We offer data licenses that allow our data partners to access, search and analyze historical and real-time data on our platform. Since the first Tweet, our users have created over 300 billion Tweets spanning nearly every country. Our data partners use this data to generate and monetize data analytics, from which data partners can identify user sentiment, influence and other trends. For example, one of our data partners applies its algorithms to Twitter data to create and sell products to its customers that identify activity trends across Twitter which may be relevant to its customers’ investment portfolios.
We have aligned our growth strategy around the three primary constituents of our platform: users, platform partners and advertisers.
We believe that there is a significant opportunity to expand our user base. Industry sources estimate that as of 2012 there were 2.4 billion Internet users and 1.2 billion smartphone users, of which only 215 million are MAUs of Twitter.
Geographic Expansion— We plan to develop a broad set of partnerships globally to increase relevant local content on our platform and make Twitter more accessible in new and emerging markets.
Mobile Applications— We plan to continue to develop and improve our mobile applications to drive user adoption of these applications.
Product Development —We plan to continue to build and acquire new technologies to develop and improve our products and services and make our platform more valuable and accessible to people around the world. We also plan to continue to focus on making Twitter simple and easy to use, particularly for new users.
We believe growth in our platform partners is complementary to our user growth strategy and the overall expansion of our platform.
Expand the Twitter Platform to Integrate More Conten— We plan to continue to build and acquire new technologies to enable our platform partners to distribute content of all forms.
Partner with Traditional Media —We plan to continue to leverage our media relationships to drive more content distribution on our platform and create more value for our users and advertisers.
We believe we can increase the value of our platform for our advertisers by enhancing our advertising services and making our platform more accessible.
Targeting— We plan to continue to improve the targeting capabilities of our advertising services.
Opening our Platform to Additional Advertisers— We believe that advertisers outside of the United States represent a substantial opportunity and we plan to invest to increase our advertising revenue from international advertisers, including by launching our self-serve advertising platform in selected international markets.
New Advertising Formats— We intend to develop new and unique ad formats for our advertisers. For example, we recently introduced our lead generation and application download Twitter Cards and Twitter Amplify, which allows advertisers to embed ads into real-time video content.
Risks Associated with Our Business
Our business is subject to numerous risks and uncertainties, including those highlighted in the section titled “Risk Factors” immediately following this prospectus summary. These risks include, but are not limited to, the following:
–If we fail to grow our user base, or if user engagement or the number of paid engagements with our pay-for-performance Promoted Products, which we refer to as ad engagements, on our platform decline, our revenue, business and operating results may be harmed;
–If our users do not continue to contribute content or their contributions are not valuable to other users, we may experience a decline in the number of users accessing our products and services, which could result in the loss of advertisers and revenue;
–We generate the substantial majority of our revenue from advertising, and the loss of advertising revenue could harm our business;
–If we are unable to compete effectively for users and advertiser spend, our business and operating results could be harmed;
–Our operating results may fluctuate from quarter to quarter, which makes them difficult to predict;
–User growth and engagement depend upon effective interoperation with operating systems, networks, devices, web browsers and standards that we do not control;
–If we fail to expand effectively in international markets, our revenue and our business will be harmed;
–We anticipate that we will expend substantial funds in connection with the tax liabilities that arise upon the initial settlement of restricted stock units, or RSUs, in connection with this offering, and the manner in which we fund that expenditure may have an adverse effect on our financial condition; and
–Existing executive officers, directors and holders of 5% or more of our common stock will collectively beneficially own _____% of our common stock and continue to have substantial control over us after this offering, which will limit your ability to influence the outcome of important transactions, including a change in control.
Channels for Disclosure of Information
Investors, the media and others should note that, following the completion of this offering, we intend to announce material information to the public through filings with the Securities and Exchange Commission, or the SEC, our corporate blog at blog.twitter.com, the investor relations page on our website, press releases, public conference calls and webcasts. We also intend to announce information regarding us and our business, operating results, financial condition and other matters through Tweets on the following Twitter accounts:_____, _____ and _____.
The information that is tweeted by the foregoing Twitter accounts could be deemed to be material information. As such, we encourage investors, the media and others to follow the Twitter accounts listed above and to review the information tweeted by such accounts.
Any updates to the list of Twitter accounts through which we will announce information will be posted on the investor relations page on our website.
Twitter, Inc. was incorporated in Delaware in April 2007. Our principal executive offices are located at 1355 Market Street, Suite 900, San Francisco, California 94103, and our telephone number is (415) 222-9670. Our website address is www.twitter.com. Information contained on, or that can be accessed through, our website does not constitute part of this prospectus and inclusions of our website address in this prospectus are inactive textual references only.
“Twitter,” the Twitter bird logo, “Tweet,” “Retweet” and our other registered or common law trademarks, service marks or trade names appearing in this prospectus are the property of Twitter, Inc. Other trademarks and trade names referred to in this prospectus are the property of their respective owners.
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Here are 57 public repositories matching this topic..., humansignal / label-studio.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
- Updated Nov 15, 2023
doccano / doccano
Open source annotation tool for machine learning practitioners.
- Updated Oct 26, 2023
argilla-io / argilla
✨Argilla: the open-source feedback platform for LLMs
UniversalDataTool / universal-data-tool
Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
- Updated May 3, 2022
code-kern-ai / refinery
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
- Updated Sep 20, 2023
dbpedia-spotlight / dbpedia-spotlight
DBpedia Spotlight is a tool for automatically annotating mentions of DBpedia resources in text.
- Updated Mar 8, 2018
DataTurks-Engg / Entity-Recognition-In-Resumes-SpaCy
Automatic Summarization of Resumes with NER -> Evaluate resumes at a glance through Named Entity Recognition
- Updated Oct 31, 2019
EricGuo5513 / HumanML3D
HumanML3D: A large and diverse 3d human motion-language dataset.
- Updated Jun 24, 2023
recogito / recogito-js
- Updated Oct 6, 2023
label-sleuth / label-sleuth
Open source no-code system for text annotation and building of text classifiers
samueldobbie / markup
A web-based document annotation tool, powered by GPT-4 🚀
- Updated Oct 18, 2023
d5555 / TagEditor
🏖TagEditor - Annotation tool for spaCy
- Updated Sep 23, 2022
leifeld / dna
Discourse Network Analyzer (DNA)
- Updated Oct 27, 2023
mit-ccc / TweebankNLP
[LREC 2022] An off-the-shelf pre-trained Tweet NLP Toolkit (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Tweebank-NER dataset
- Updated Jun 30, 2022
jkkummerfeld / slate
A Super-Lightweight Annotation Tool for Experts: Label text in a terminal with just Python
- Updated May 8, 2023
etalab / piaf
Question Answering annotation platform - Plateforme d'annotation
- Updated Apr 30, 2021
doccano / doccano-client
A simple client for doccano API.
- Updated Oct 17, 2023
matryer / anno
Go package for text annotation.
- Updated Feb 4, 2020
CyberAgent / fast-annotation-tool
FAST is an annotation tool that focuses on mobile devices. https://aclanthology.org/2021.emnlp-demo.41/
- Updated Nov 4, 2021
vmware / data-annotator-for-machine-learning
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning
- Updated Jul 27, 2023
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Top 6 Text Annotation Tools
Annotation Lab is now the NLP Lab – the Free No-Code AI by John Snow Labs
The development of high-quality Deep Learning NLP models usually requires significant amounts of training data. The models must be taught to correctly differentiate specific entities and make accurate predictions. This is usually done via examples (training data) provided by human users, with good expertise in the target domain. The best and easiest way to put together the example data is via (manual) annotation.
Bottlenecks of text annotation
Managing and streamlining data annotation is not an easy task. It comes with several challenges and obstacles that can seriously affect the success of any AI project. Following are some common data annotation challenges that impact the team productivity and models’ quality:
- AI and ML models are data-hungry and need a significant amount of labeled data to learn from. Thus, businesses struggle to secure and manage a highly specialized workforce for generating labeled data to feed the models.
- For annotating documents and preparing the annotations in the expected format to feed into the training pipelines, specialized tools are necessary to improve productivity and ensure coherence and inter-annotator agreement. Developing such tools from scratch is a highly specialized, effort-intensive, and time-consuming process. So is the maintenance of such a tool.
- For training Deep Learning models, skilled data scientists are needed to check the quality of the annotations, train and tune the models and deploy them in production. Such professionals are hard to find and expensive to retain.
Choosing the right tools
This blog compares some of the most commonly used solutions for Text Annotation available on the market and highlights their major features and limitations.
The tools included in this comparison are:
- NLP Annotation Lab
- Label Studio
For choosing the most suitable solution for your particular annotation problem, start by answering the following questions:
- What content do I need to process?
- How do I manage my team and projects?
- How do I keep my data safe?
- How can I automate the annotation process?
- How much am I willing to pay for my text annotation tool ?
Supported Content Types
The starting point of any annotation project is to analyze the documents that need to be processed both in terms of content and modality. Are you analyzing text, video or audio content? And what entities do you need to extract/annotate: named entities, relations, bounding boxes, etc.
When comparing the support for different content types, Annotation Lab and Label Studio offer the same level of features in the free versions (for example they both contain image annotation tool ), while Prodigy includes those in the paid edition. LabelBox is missing support for audio content while LightTag and TagTog do not offer any image, video, or audio annotation features.
Projects and Teams
When working on complex data extraction/validation projects, usually, the work is distributed among a team of domain experts with the role of annotators or reviewers. Such collaboration demands using a software tool for effective project management, including task assignment, tracking, and quality checking.
Among the 6 tools included in the comparison, the largest palette of project management features by far is offered by the Annotation Lab. All those features are included in the community (free) version of the tool.
While the other text annotation tools also cover some important features (e.g support for multiple projects, API access) they are very often included in the paid editions (see the case of LightTag, Prodigy or TagTog). Another example is that of Task Assignments — a mandatory feature when running team-based projects — which is only available in the free versions of the Annotation Lab , LabelBox, and TagTog.
Projects and Teams Features
The situation is very similar when looking at collaboration features such as consensus analysis, feedback and comments features, out-of-the-box review workflows, and performance dashboards. All those functionalities are available in the Annotation Lab for free, while the other tools, if they include the features, those are part of the enterprise/paid editions.
Security and Privacy
When annotating enterprise data you are often faced with the need to handle Personal Identifying Information (PII) and Protected Health Information (PHI) in a secured and privacy-aware setup. This often means you will need to deploy the NLP annotation tool on your own premise and avoid data sharing or SAAS setups.
Among the 6 tools compared here, Annotation Lab is the only one that offers enterprise-grade security and privacy features for free:
- Zero data sharing
- Role-based access
- Full audit trails
- Multi-factor authentication.
LightTag and TagTog are right behind with Enterprise support for the majority of listed features except for annotation versioning. This makes it difficult to run experiments on your projects with different versions of the data.
Security and Privacy Features
Pre-annotation is the process of generating annotations for a set of documents/tasks using an existing model before a human annotator manually completes/corrects/validates them. It results in crucial time savings for annotators as it increases the annotation speed.
This feature is freely available in John Snow Labs’ Annotation Lab platform. Annotation Lab facilitates an end-to-end process from document import, pre-annotation, manual corrections, and manual annotation to model training and testing without writing a line of code. It also offers seamless integration with the NLP Models Hub, from where users can download and reuse hundreds of pre-trained models so they don’t waste time on already learned tasks.
Model-based preannotation is also possible in LabelStudio via third-party ML integrations that need to be setup by users. LabelBox, LightTag, TagTog, and Prodigy only offer this type of automation on the paid versions.
No Code Model Training
If you want to go beyond annotated data and obtain a fully functional, production-ready NLP model, the only platform that allows you to do that without getting Data Scientists involved and without writing a line of code is the Annotation Lab.
Once enough training data is available (e.g. your team annotated at least 40–50 examples for each entity in your taxonomy) you can start training a new model . This can be done from scratch or by tuning an already existing pretrained model.
Annotation Lab also offers active learning features, which trigger the model training automatically in the background when target milestones are reached. It can be configured to run when 50, 100 or 200 new completions are available.
Getting it All for Free
At the time of this comparison, 5 out of the 6 tools offered free versions but 4 of them impose important limitations on the available features.
Tools Editions and Limitations
If you need a flexible and powerful end-to-end platform for document annotation and model training that you can deploy on the cloud or on your premise with enterprise-level security and privacy features and no limitations on the number of projects, tasks, users, models, and pre-annotations you should definitely choose Annotation Lab. This is very suited for both data scientists and domain experts as all features it offers are available via both UI and API.
Annotation Lab can be installed for free via AWS and Azure Marketplaces . You can also install it locally on any ubuntu server by following the instructions detailed here .
Benchmarking information for NER and Classification Models in Annotation Lab 4.4.0
Recommended For You
Text Annotation Tools for NLP
The goal of this article is to give a quick overview of the tools available to you to perform Text Annotation (Data Labeling) in the Natural Language Processing ( NLP ) field.
This article is an extract of my previous article where I give a general overview of what NLP is, I recommend checking this article first if you are not familiar with NLP.
What is NLP?
In a nutshell, NLP is a field of Machine Learning focused on extracting insights from natural language . Your goal is to make computers understand our own language .
Some practical examples of NLP are speech recognition, translation, sentiment analysis, topic modeling, lexical analysis, entity extraction and much more.
Using all these tools and algorithms you can extract structured data from natural language , data that can be processed by computers. Furthermore, the output of NLP tasks if often a machine learning algorithm that will use this raw data to make predictions .
By combining many algorithms together , you can extract useful data that can be used in a wide range of scenarios such as:
- Fraud Detection
- Risk Intelligence
- Email Classification
- Sentiment Analysis
For supervised algorithms such as text classification or NER , you will need to label your text data. You will need some tool to help you with this task. Let’s review some of these tools…
Doccano is a web-based, open-source annotation tool. Doccano gives you the ability to have it self-hosted which provides more control as well as the ability to modify the code according to your needs. It supports different teams and it is very easy to use . You can try a demo here .
Check my previous article for more details about Doccano. You can follow Doccano instructions to install this open source tool.
Doccano is great if you are not a programmer, anyone can get up to speed quickly and collaborate to speed up the labeling process. I recommend using Doccano if the people with the business domain knowledge are not coders and the complexity of the business domain is high . In this case you can use Doccano to manually label the data in parallel by the members of the team who have the business knowledge.
- Easy to use
- Support Teams
- Easy to install
- Open Source
- Fully manual annotation
Prodigy was created by the same team behind SpaCy . It is a modern annotation tool for creating training and evaluation data for machine learning models. It is more than an annotation tool , it is integrated with SpaCy and can be used to train models as well. It is target to data scientists who has Python programming knowledge.
Prodigy is powered by active learning which means it provides semi-automation . You can start by labeling few samples and the active learning model will try to learn and tag the rest of the data set for you, so you can only indicate if a sample is correct or not. Furthermore, it will suggest the best samples based on information gain, so you don’t waste time with samples that will not improve the model predictions. You can check a live demo here .
With Prodigy, you label and train the model in a fast and iterative process removing a lot of manual work. It merges the labeling and training process so experts can label the data in an useful and meaningful way instead of outsourcing the labeling process and wasting a lot of time in labeling unnecessary text samples. This way you can try ideas very quickly.
Prodigy can be used to tag named entities for NER , text for classification and even images, video and sound!. It comes with many “ recipes ” which are workflows to perform certain task, check this flowchart to examine them.
In a nutshell the process is as follows:
- First, you need to get your sample text data JSONL which each line contains one entry, this is an example .
- The next step is to start the manual annotation, for example for NER, you will run this in the command line:
- This will start ner.manual Prodigy recipe so you can start labeling, this will open a web server with the Web UI in: http://localhost:8080
- Now you can start annotating:
- After annotating some examples, you can train the model:
- The first argument defines the component to train: in this case, ner . You also need to pass in the name of the dataset with the annotations to train from and the base model to start with. In this example, the last argument is using a large en_vectors_web_lg model as the base model. The vectors will be used as features during training, which can give you a nice boost in accuracy. If you don’t have the vector package installed yet, you can download it via spacy download en_vectors_web_lg
- Based on the results metrics such accuracy or f-score , you can decide what are your next steps. Prodigy has many recipes to help you along the way, some can be used to detect if tagging more data will improve your model or not, others will try to tag for you so you just have to accept or reject, other’s will detect the most meaningful examples from your data set, and so on. Check this chart for more details.
As you can see, Prodigy is very powerful and has great compatibility with SpaCy . These two tools allow you to implement full end to end NLP solutions for your organization. Note that Prodigy is not open source , you need to get a license so you can download and install it in your environment.
I recommend Prodigy to Data Scientist teams who also has business knowledge and can label the data themselves. It works particularly well for small teams who need fast iterations .
- Automation: it can really speed up the NLP process.
- Lots of features
- Can train the models
- Learning Curve
- Not Open Source.
- TagTog : web-based text annotation tool, no installation is required. It has a cloud offering. Support for group annotation for teams. It also has Machine learning capabilities: learns from previous annotations and automatically generates similar annotations.
- Brat : open source free annotation tool.
We can try to summarize NLP by saying that it combines a set of tools and techniques to transform complex natural language in machine readable data. To do this for supervised machine learning models, we need to provide a training set with labelled data. We use annotation tools to do this. For big organizations with complex business models who have the resources to perform manual testing, Doccano can be a good option. For smaller data scientist teams, Prodigy will be their best choice.
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Written by Javier Ramos
Certified Java Architect/AWS/GCP/Azure/K8s: Microservices/Docker/Kubernetes, AWS/Serverless/BigData, Kafka/Akka/Spark/AI, JS/React/Angular/PWA @JavierRamosRod
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Top 6 Text Annotation Tools - NewsCatcher
Annotation is the part where most projects stall, and it can make or break your models. Here are some tools and tips to help you with text annotation needs.
Even with all the recent advances in machine learning and artificial intelligence, we can’t escape the irony of the information age. In order for humans to rely on machines, machines need humans first to teach them. So if you're doing any type of supervised learning in your natural language processing pipeline, and you most likely are, data annotation has played a role in your work. Maybe you were lucky enough to have a large pre-annotated text corpus. And you didn't need to do all the text annotation for training yourself. But if you want to know how well it's doing in production, you'll have to annotate text at some point.
What Is Text Annotation?
Text annotation is simply reading natural language data and adding some additional information about it, in a machine-readable format. This additional information can be used to train machine learning models and to evaluate how well they perform.
Let’s say you have this piece of text in your corpus: “I am going to order some brownies for tomorrow”
You might want to identify that brownies are a food item and/or that tomorrow is the delivery time. Then use that piece of information to ensure that you have some brownies for them and that you can deliver them tomorrow.
Or maybe your task is on a larger scale. So you might want to annotate that the whole sentence has the intent of placing an order.
Tips To Make Your Text Annotation Process Better
The first thing you can do to make the life of your annotators and developers simple is to keep the labels simple and descriptive. food_item and time_of_delivery are good, straightforward labels that describe what you’re annotating. But labels like intent_1 , intent_1_ver2 , and unnecessary acronyms make it harder to quickly apply and check labels.
Besides that, it’s unlikely that one person is going to be annotating everything on their own. Usually, there is a team of people that need to agree on what the labels mean. I recommend that you define your labels in a central shared location and keep this information up to date. So if a new label is added, or if the meaning of a label changes, everyone has easy access to the updates.
Checking The Quality Of Your Text Annotations
One often overlooked thing is checking the quality of your annotations. Well, how does one even do that? You could go through all of the text again, but that’s inefficient.
One handy technique is to use a flag to denote confusion or uncertainty about an annotation. This enables annotators that are unsure about an annotation to flag it, allowing it to be double-checked later.
Another helpful method is to have some annotators look at the same data, and compare their annotations. You could use a measure of inter-rater reliability like Cohen's kappa , Scott's Pi , or Fleiss's kappa for this. Or you could create a confusion matrix.
In the example above, annotator 1's labels are in the columns and annotator 2's labels are in the rows. You can see that they both agree on all the things labeled order_time , and they mostly agree on the food_item . But there seems to be a lot of confusion about where the label food_order should be applied.
This might be a sign that the label needs more clarification about its meaning, or that it needs to be slit into separate labels. Or maybe it should be removed completely.
Top Text Annotation Tools
Brat (browser-based rapid annotation tool).
brat is a free, browser-based online annotation tool for collaborative text annotation. It has a rich set of features such as integration with external resources including Wikipedia, support for automatic text annotation tools, and an integrated annotation comparison. The configurations for a project-specific labeling scheme is defined via .conf files , which are just plain text files.
brat is more suited to annotating expressions and relationships between them, as annotating longer text spans like paragraphs is really inconvenient (the pop-up menu becomes larger than the screen). It only accepts text files as input documents, and the text file is not presented in the original formatting in the UI. So it is not suitable for labeling structured documents like PDFs.
It comes with detailed install instructions and can be set up in a few lines of code.
To set up the standalone version, just clone the GitHub repository :
Navigate into the directory and run the installation script:
You’ll be prompted for information like username, password, and admin contact email. Once you have filled in that information, you can launch brat:
You will then be able to access brat from the address printed in the terminal.
doccano is an open-source, browser-based annotation tool solely for text files. It has a more modern, attractive UI, and all the configuration is done in the web UI. But doccano is less adaptable than brat. It does not have support for labeling relationships between words and nested classifications, however, most models and use cases don’t need these anyway.
You can write and save annotation guidelines in the app itself and use keyboard shortcuts to apply an annotation. It also creates a basic diagrammatic overview of the labeling stats. All this makes doccano more beginner, and in general user, friendly. It does support multiple users, but there are no extra features for collaborative annotation.
The setup process is also quite simple, just install doccano from PyPI:
After installation, run the following commands:
In another terminal, run the following command:
And go to http://127.0.0.1:8000/ in your browser.
LightTag is another browser-based text labeling tool, but it isn’t entirely free. It has a free-for-all version with 5,000 annotations a month for its basic functionalities. You just need to create an account to start annotating.
The LightTag platform has its own AI model that learns from the previous labeling and makes annotation suggestions. For a fee, the platform also automates the work of managing a project. It assigns tasks to annotators, and ensures there is enough overlap and duplication to keep accuracy and consistency high.
What really makes LightTag stand out, in my opinion, is its data quality control features. It automatically generates precision and recall reports of your annotators, and has a dedicated review page that enables you to visually review your teams' annotations. LightTag also detects conflicts and allows you to auto-accept by majority or unanimous vote.
You can also load your production model’s predictions into LightTag and review them to detect data drift and monitor your production performance. It was recently acquired by Primer.ai , so you get access to their NLP platform with the subscriptions as well.
TagEditor is a standalone desktop application that enables you to quickly annotate text with the help of the spaCy library. It does not require any installations. You just need to download and extract the TagEditor.7z file from their GitHub repo , and run TagEditor.exe . Yes, it is limited to Windows 😬
With TagEditor you can annotate dependencies, parts of speech, named entities, text categories, and coreference resolution, create your customized annotated data or create a training dataset in .json or .spacy formats for training with spaCy library or PyTorch. If you're working with spaCy on Windows, TagEditor covers all bases.
tagtog is a user-friendly web-based text annotation tool. Similar to LigthTag, you don’t need to install anything because it runs on the cloud. You just have to set up a new account and create a project. But if you need to run it in a private cloud environment, you can use their Docker image.
It provides free features to cover manual annotation, train your own model with Webhooks, and a bunch of pre-annotated public datasets. tagtog accelerates manual annotation by automatically recognizing and annotating words you've labeled once.
You can upload files in the supported format , such as .csv , .xml , .html , or simply insert plain text.
There is a subscription fee for the more advanced features like automatic annotation, native PDF annotations, and customer support. tagtog also enables you to import annotated data from your own trained models. You can then review it in the annotation editor and make the necessary modifications. Finally, download the reviewed documents using their API and re-train your model. Check out the official tutorials for complete examples.
The folks at Explosion.ai (the creators of spaCy ) have their own annotation tool called Prodigy . It is a scriptable annotation tool that enables you to leverage transfer learning to train production-quality models with very few examples. The creators say that it's "so efficient that data scientists can do the annotation themselves." It does not have a free offering, but you can check out its live demo .
The active learning aspect of this annotation tool means that you only have to annotate examples the model doesn’t already know the answer to, considerably speeding up the annotation process. You can choose from . jsonl , . json , and . txt formats for exporting your files.
To start annotating, you need to get a license key , and install Prodigy from PyPI:
And if you work with JupyterLab, you can install the jupyterlab-prodigy extension.
The extension enables you to execute recipe commands in notebook cells and opens the annotation UI in a JupyterLab tab, so you don’t need to leave your notebook to annotate data.
Prodigy is not limited to text, it enables you to annotate images, videos, and audio. It also has an easy-to-use randomized A/B testing feature that you can use to evaluate models for tasks like machine translation, image captioning, image generation, dialogue generation, etc.
If you can't spend any money, and your annotation task is something simple go with doccano. And if you need to label relationships go with TagEditor, but if you want more control and customization you can use brat.
On the paid tools front, Prodigy is the best option if you are willing to write some code to create data quality reports and manage annotation conflicts. While Prodigy does look like a pricey option upfront, it is worth noting that it is a one-time fee for a lifetime license with one year of updates. On the other hand, tagtog and LightTag are subscription services. But if you want a more ready out-of-the-box solution, you can go with tagtog or LightTag.
Text annotation for machine learning [Updated 2023]
Despite the massive shift towards digitization, some of the most complex layers of data are still stored in the form of text on paper or official documents. With the plethora of publicly available information, there comes the challenge of managing unstructured, raw data and making it understandable for machines. Unlike images or videos, texts are more complicated. Let's take a sample sentence: “They nailed it!”. Humans are expected to understand it as applause, encouragement, or appreciation, while the traditional Natural Language Processing (NLP) model is likely to perceive the surface-level representation of the word, missing out on the intended meaning. Namely, it may associate the word nail with hammer nailing. Accurate text annotations help models better grasp the data provided, resulting in an error-free interpretation of the text. We will use this opportunity to build up your knowledge of this integral type of data annotation by covering the fundamentals as listed below:
What is text annotation?
Why is it important, how is text annotated: nlp text annotation, text annotation for ocr, types of text annotation, use cases of text annotation, final thoughts.
Text annotation is the machine learning process of assigning labels to a text document or different elements of its content to identify the characteristics of sentences. As intelligent as machines can get, human language is sometimes hard to decode, even for humans. In text annotation , sentence components, or structures are highlighted by certain criteria to prepare datasets to train a model that can effectively recognize the human language, intent, or emotion behind the words. The training data is given to machine learning so they can comprehend various aspects of sentence formation and conversations between humans.
You might still wonder; why do we need to annotate text at all? Recent breakthroughs in NLP highlighted the escalating need for textual data for applications as diverse as insurance, healthcare, banking, telecom, and so on. Text annotation is crucial as it makes sure that the target reader, in this case, the machine learning (ML) model, can perceive and draw insights based on the information provided. As the world becomes more digitized, data quality needs also increase rapidly. Businesses must learn how to get the best use of the large amounts of data that are provided to their platforms to stand out in the market. Not to mention the increasing demand of customers for digitized and timely support services. We'll take a deeper dive into particular use cases later in this post, but for now, keep the following in mind: textual data is still data—much like images or videos—and is similarly used for training and testing purposes.
The list of tasks computers are taught to perform increases steadily, yet some activities still remain untackled: natural language processing (NLP) is no exception to that. Without human annotators, models won't acquire the depth, nativity, and even slang in which humans craft, control, and manipulate language. That's why companies continuously turn to human annotators to ensure sufficient amounts of quality training data . Current NLP-based artificial intelligence (AI) solutions cover voice assistants, machine translators, smart chatbots, and alternative search engines, yet the list keeps expanding in parallel with the flexibility text annotation types propose.
Optical character recognition (OCR) is the extraction of textual data from scanned documents or images (PDF, TIFF, JPG) into model-understandable data. OCR solutions are aimed at easing the accessibility of information for users. It benefits business operations and workflows, saving time and resources that would otherwise be necessary to manage unsearchable or hard-to-find data. Once transferred, OCR-processed textual information can be used by businesses more easily and quickly. Its benefits include the elimination of manual data entry, error reduction, improved productivity, etc.
We've explored OCR and its applications further in a separate article. The major takeaway for now: OCR along with NLP are the two primary areas that heavily rely on text annotation.
Text annotation datasets are usually in the form of highlighted or underlined text, with notes around the margins. Here are the main types of text annotation we'll cover in this post:
Entity annotation is the process of assigning entities in text with their corresponding predefined labels based on their semantic meaning. The annotated text is then provided to machine learning models to retrieve the underlying meaning of text data entities. This type of annotation can be described as locating, extracting, and tagging entities in text in one of the following ways:
Named entity recognition (NER): NER is a technique to label key information from the text, be it people, geographic locations, frequently appeared objects, or characters. We talked briefly about NER in our data annotation article, so let's discuss a similar example and use it to describe more cases.
As simple as that - we describe entities "SuperAnnotate" and "CB insights" as companies, "2021" as date, and depending on the variety of entities you need to extract from the text - the list may be continued.
"SuperAnnotate was among the top 100 AI companies, and top 3 annotation companies according to CB insights in 2021"
NER is fundamental to NLP - Google Translate, Siri, and Grammarly are excellent examples of NLP that use NER to understand textual data.
Coreference resolution(relationship annotation) : This is a similar approach to NER, except coreference resolution maps the entities which mean the exact same thing.
"SuperAnnotate was among the top 100 AI companies, and top 3 annotation companies according to CB insights in 2021. This was a major motivation for the company"
In this example, "company" is used to refer to "SuperAnnotate", thus they mean the exact same thing.
Coreference resolution is used in NLP tasks such as sentiment analysis , question answering, text summarization, etc. Without accurate coreference resolution, automated systems may misinterpret the meaning of a text or miss important information, leading to reduced performance and accuracy. By accurately identifying and linking all mentions of the same entity, coreference resolution helps improve the quality of natural language processing tasks.
Part-of-speech tagging: As the name suggests, part-of-speech tagging assists in parsing sentences and identifying grammatical units, such as nouns, verbs, adjectives, pronouns, adverbs, prepositions, conjunctions, etc. Although this seems pretty trivial, there are many tricky linguistic cases when one word represents various parts of speeches, such as the word "book", commonly used as a noun such as in "I loved reading that book", but also as a verb in "We need to book a ticket asap".
Keyphrase tagging: Keyphrase tagging is the action of locating and labeling keywords or keyphrases in textual data. Imagine you open a big text document and need to know the key concepts discussed in it without reading the whole thing. This and many other NLP tasks require keyphrase tagging to come into play.
Although entity annotation is a blend of entity, part-of-speech, and keyphrase recognition, it often goes hand-in-hand with entity linking to help models contextualize entities further.
Entity linking is the process of mapping words in a text to entities in the knowledge base. Don't get confused about the ambiguity of "knowledge base": it is usually referred to as open-domain texts derived from Wikipedia.
If entity annotation helps locate or extract entities in text, entity linking, also referred to as named entity linking (NEL), is the process of connecting these named entities to bigger datasets. Take the sentence "Summer loves ice cream." The point is to determine that Summer refers to the girl's name and not the season of the year or any other entity that can potentially be referred to as Summer. Entity linking differs from NER in the sense that NER spots the named entity in the text but does not specify which entity it is.
To make sure we understand NEL and can differentiate it from NER, let's test our knowledge of the previous example sentence.
Here, CB insights would be mapped to its Wikipedia page. In the case of SuperAnnotate, however, since it's a specific brand/product name and is not included in a general-purpose knowledge base like Wikipedia, entity linking becomes more complex. A common way to handle such cases is to provide a related link that will best describe the entity.
While entity annotation refers to the process of annotating particular words or phrases, text classification refers to annotating a chunk of text or lines with a single label. Examples and rather specialized forms of text classification include document classification, product categorization, sentiment annotation, and so forth.
Let's look at each of these forms separately.
Document classification: Assigning the document a single label can be useful for the intuitive sorting of massive amounts of textual content.
Product categorization: The process of sorting products or services into classes and categories can improve search results for eCommerce. For instance, brush up on the SEO and boost the product's visibility on the rankings page.
Email classification: Classifying emails as spam or non-spam (ham) based on their content.
News article classification: Categorizing news articles based on their topics such as politics, entertainment, pop culture, etc.
Language identification: Determining the language of a given text.
Toxicity classification: Identifying whether a social media comment or post is toxic or non-toxic or whether it contains hate speech.
As the name implies, sentiment annotation is about determining the emotion or opinion behind the text body. Sometimes, it's even difficult for us, humans, to figure out the meaning of the message received, especially if sarcasm or other forms of language manipulation is inherent in the text. Imagine a machine detecting that! The behind-the-scenes of this phenomenon is an annotator closely analyzing the text, picking the label that best represents the emotion, sentiment, or opinion. Computers later base their conclusions on analogous data to differentiate positive, neutral, and negative reviews or other kinds of textual information. In light of the applicability, sentiment analysis helps businesses develop strategies around how their product or service is positioned in the marketplace and how to track it further.
Let's explore a few examples of sentiment annotation.
In the first two cases, emotions are clear - the first one gives happiness and positivity, while the second is about disappointment and negative emotions. In the case of the third example, classifying an exact one type of emotion would be biased, since "nostalgic" and "bittersweet" do not imply an either-or approach, but rather mixed feelings. Note that this is not the only case when sentiment annotation meets challenges. Here are some other tricky scenarios :
Success or failure of one side. Take the sentence "Yay! Argentina beat France in the World Cup Finale." At first glance, emotions seem to be very positive, but let us not forget that the sentence indicates failure and negative feelings of the opposite side.
Sarcasm and ridicule. Sarcasm is a uniquely human communication style and requires knowledge of context, tone of voice, and social cues which humans are gifted to differentiate. It takes a lot of effort to teach this to machines.
Rhetorical questions . "Why do we have to quibble every time?" Again, at first, this tweet seems to be a neutral question, but from the way the speaker delivers the question, we can detect a sense of frustration and negativity.
Quoting somebody else or re-tweeting: For quotes and retweets, the confusion lies in the fact that the one who quotes does not necessarily hold the same opinion as the one who wrote the quote. Thus, the classified emotion might not express reality.
The use cases of text annotation are almost as all-around as those of image annotation and video annotation. Roughly every discipline that contains textual data can be annotated and used for model training:
Text annotation is a game-changer in healthcare as it replaced heavy manual processes with high-performing models. Particularly, it impacts the following operations:
- Automatic data extraction from clinical trial records as well as classification of medical documents for better access and ease of research.
- Improved patient outcomes through thoroughly analyzed patient records and better medical condition detection.
- Recognition of medically insured patients, loss amount, and further policyholder information to process claims faster.
Similar to healthcare, text annotation has numerous benefits for the insurance industry .
- Risk evaluation and extraction of contextual data from contacts and forms.
- Recognition of entities like involved parties and loss amount for faster claims processing.
- Claims fraud detection and monitoring of documents and forms to identify dubious claims.
Increased personalization, higher automation, reduced error rates, and adequate resource utilization are not miles away. A model fed by accurate text annotations makes all that possible through:
- Identification of fraud and money laundering patterns.
- Streamlined workflows through extraction and management of custom data from contracts.
- Extraction of loan rates, credit scores, or other attributes to monitor compliance.
As broad as this sector is, text annotation provides various benefits for the domain:
- More efficient financial operations as text annotation provides smooth regulatory compliance with advanced analytics.
- Better and easier access to digital documents through text classification, and that includes the classification processes of different kinds of legal cases.
- Early detection of any possible defrauding activities through linguistic annotation, semantic annotation, tone detection, and much more.
- The ability to draw analytics from volumes of data through entity recognition.
With the growth of the logistics industry, its usage of technology expands with it. Large amounts of data are generated every day in this industry, whether it is from invoices to chatbots and online assistants.
Text Annotation in logistics is used to:
- Annotate amounts, order numbers, names, and more from the invoices.
- When it comes to customer feedback, it uses both sentiment annotation and entity annotation.
With the growing demand for faster and more reliable news, text annotation is being heavily used in the news media industry and its use cases include:
- Text classification to categorize the content.
- Entity annotation to annotate the names, key phrases, and numbers from different news articles.
- By using text annotation such as NLP annotation, sentiment analysis, and other AI annotation models, news content becomes more recognizable, and detecting fake news becomes easier.
- Both semantic annotation and linguistic annotation are used for annotating semantics, phonetics, and news article discourse.
Last but not least, annotated text automates extensive human-powered work in the following areas:
- Network performance optimization and accurate issue prediction.
- Automotive responses to client queries, including chat and email.
- Comprehensive analysis of network interactions.
- Understanding customer intent and sentiment to provide better support adhering to all KPIs and metrics of your support center.
- Detection of malicious activity, if any.
- Personalized promotion and product creation based on customer behavior analysis.
How to annotate texts with SuperAnnotate
With all the information we provided above, you now have a basic understanding of how the process of text annotation goes, and as you have guessed, it can be pretty complicated. Here is how your text annotation process can become smoother and less complex with SuperAnnotate:
- We offer a top-notch document classification with fast access and instinctive categorization that enables enhanced performance.
- An NER that recognizes common or custom entities in a text body promptly.
- A smooth information extraction process whether from unstructured text, PDF, tables, or any other documents.
- A thorough sentiment analysis that detects sentiments starting with words to long documents.
- Annotation of question-answer pairs which generates an intelligent chatbot system and gives quick answers.
- The ability to translate text inputs into languages of interest.
The text annotation process in SuperAnnotate consists of a few simple steps.
1. Project Setup: Assuming you already have a team on the SuperAnnotate platform (you can learn more about this in our documentation), your next step would be creating a project on the upper right panel and then clicking on Text. Give a concise descriptive name to your project and click Create. This video demonstrates project setup step by step: First, you create a class and give names according to the classes. In our example, we're annotating a text about Bob Marley, and we particularly care about names, locations, dates, and song names.
2. Data Upload: The text data upload procedure is done through integrations or URL attachments.
3. Text annotation: And finally, the actual annotation task. The technique is as simple as shown in the video - you just select the entities and assign them to their corresponding classes by right-clicking and choosing the class name. You can then use the annotated data for your project.
But why SuperAnnotate and not other platforms?
Despite being fairly new to the market, SuperAnnotate’s state-of-the-art resources and annotation skills led it to be one of the market-leading annotation platforms. Let's take a sneak peek into some of SuperAnnotate's key services:
SuperAnnotate’s DataOps platform offers automated labeling, unparalleled control of annotation workflow, quick detection of data quality issues, and tops it all off with pipeline integration.
As professionals in the field, our team is trained on SuperAnnotate’s software, offering flawless leverage to the platform features. SuperAnnotate’s marketplace provides a comparison between the best annotation teams for any project, maximum speed, and the highest quality delivery while also delivering a fully managed marketplace.
MLOps success program
We deliver DevOps and machine learning expertise to serve as an extension of the user’s existing data engineering unit and set their best practices in motion. By choosing our platform, users receive best-in-class customer success and PM, ML, annotations, DevOps, pipeline support, and finally, software and services all in one place.
Text annotation does not cease to be the cherry on top across the most complicated data annotation projects. However, with the variety of types and nascent use cases topped with accurate training data, text annotation gives models the ability to read, comprehend and act upon the introduced information much like humans do. Are you also considering text annotation for your computer vision pipeline ? Don't hesitate to reach out if you need more information or further assistance at any point throughout your pipeline.
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Invoice annotation automation
What is natural language processing (NLP): Techniques and use cases
Radically efficient machine teaching. An annotation tool powered by active learning.
Train a new ai model in hours.
Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration.
Today’s transfer learning technologies mean you can train production-quality models with very few examples. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. You'll move faster, be more independent and ship far more successful projects.
The missing piece in your data science workflow
Prodigy brings together state-of-the-art insights from machine learning and user experience. With its continuous active learning system, you're only asked to annotate examples the model does not already know the answer to. The web application is powerful, extensible and follows modern UX principles. The secret is very simple: it's designed to help you focus on one decision at a time and keep you clicking – like Tinder for data.
Everyone knows data scientists should spend more time looking at their data. When good habits are hard to form, the trick is to remove the friction. Prodigy makes the right thing easy, encouraging you to spend more time understanding your problem and interpreting your results.
Try it live and highlight entities!
Try it live and select text categories, try it live and draw bounding boxes, try it live and type some text, prodigy users include, try out new ideas quickly.
Annotation is usually the part where projects stall. Instead of having an idea and trying it out, you start scheduling meetings, writing specifications and dealing with quality control. With Prodigy, you can have an idea over breakfast and get your first results by lunch. Once the model is trained, you can export it as a versioned Python package, giving you a smooth path from prototype to production.
What others say
Case Study Journalism To facilitate trust, human-in-the-loop workflows are widespread in media applications as stakeholders require the ability to teach and to evaluate models through human-AI interfaces. For their AI projects, the Guardian’s data science team decided to use Prodigy.
Cheyanne Baird NLP Research Scientist Prodigy's design aspect was key. [With my previous annotation tools], I would get a lot of feedback from annotators, saying 'it's really hard, because I have to scroll and scroll and scroll to see the labels. There's too many labels. There's too many options.' When I was looking at Prodigy I liked it because you could customize it.
Andy Halterman Researcher A lack of labeled data held geoparsing back for years. It took a week to fix that with Prodigy.
Raphael Cohen Head of Research Prodigy is by far the best ROI we had on any tool!
Case Study Finance Posh focuses on developing custom NLP models trained on real-world banking conversations and custom models for each client’s unique customer base and product offering. To get their NLP models working effectively, the team needed to emphasize human annotation and experimentation, which is why Posh turned to Prodigy.
User Survey Participant ML Engineer I really love being able to do almost everything in Python, it means that team members with no front end experience can create tasks super easily.
User Survey Participant ML Engineer Prodigy gives you solutions for the problems that you did not even know you have.
Antonio Polo de Alvarado ML Engineer I have been working with Prodigy these last few weeks and I can say that it is probably (if not the best) one of the best NLP tools.
Fully scriptable and extensible
Prodigy is fully scriptable, and slots neatly into the rest of your Python-based data science workflow. As the makers of spaCy, a popular library for Natural Language Processing, we understand how to make tools programmers love. The simple secret is this: programmers want to be able to program. Good developer tools need to let you in, not lock you out. That's why Prodigy comes with a rich Python API, elegant command-line integration, and a super productive Jupyter extension. Using custom recipe scripts, you can adapt Prodigy to read and write data however you like, and plug in custom models using any of your favourite frameworks.
Browse features, named entity recognition, span categorization, text classification, dependencies & relations, computer vision, audio & video, a/b evaluation, task routing, prompt engineering, large language models, model training.
- Natural Language Processing
The best free labeling tools for text annotation in NLP
In this blog post I'm going to present the three best free text annotation tools for manually labeling documents in NLP ( Natural Language Processing ) projects. You will learn how to install, configure and use them and find out which one of them suits your purposes best .
The tools I'm going to present are
The selection is based on this comprehensive scientific review article and our hands-on experience at dida.
I will discuss the tools one by one. For each of them, I will first give a general overview about what the tool is suited for, and then provide details (or links) regarding installation, configuration and usage.
brat rapid annotation tool
What you can do with it.
brat is an online environment for collaborative text annotation that can be run on a (possibly local) server and then used in a browser.
brat is rather meant to annotate single expressions and relationships between them, as the examples show:
Annotating significantly longer text spans (i.e. paragraphs) turns out to be really inconvenient (see Usage below).
Input documents must come as text files. The user interface (UI) presentation of the text file in brat is not necessarily true to its original formatting. For these two reasons, brat isn't a very well-suited tool for annotating structured documents, where you rather might want to annotate PDFs directly.
Configuring a labeling scheme is easy and flexible. You can define span entities, relations and attributes and constraints for them, which brat checks automatically. Furthermore, there are special configuration files to define a non-default visual configuration (e.g. colours of labels) and tools like sentence segmentation (splitting) or tokenization.
The annotations are also stored in text files. We found that parsing the annotations works smoothly if the labeled entities are words or sub-sentence expressions, but becomes tedious for longer spans.
brat provides some functionality for collaborative labeling: Multiple users are supported, and there is an integrated annotation comparison.
brat comes with detailed instructions how to install it. You find them here .
Let me just add a couple of hints that might make your life easier:
If you just want to install and run brat on your local machine, then the standalone server is what you want.
Make sure to check out the Detailed Instructions -> Placing Data section of the instructions to learn how to set up the annotation files.
brat is not compatible with Python 3. Thus you might have to modify the command python standalone.py to python2 standalone.py .
We've found that brat works best with Google Chrome .
brat allows for the configuration of a project-specific labeling scheme via .conf files (which are actually plain text files in brat's own standoff format ). How to do it is explained here .
Using brat is fairly straightforward: Marking a text span opens a pop-up menu. The options in the menu depend on the configuration of the labeling scheme.
You can try this out for yourself without installing brat.
It's not always easy to mark exactly the desired span. Furthermore, if the marked span is to long, the pop-up menu doesn't fit on the screen anymore.
More information on brat's basic functionality can be found here .
doccano is another annotation tool solely for text files. It's easier to use and simpler than brat.
Just like brat, it runs server-based and has a browser UI.
The main differences in comparison with brat are that
all configuration is done in the web user interface and
the use case is limited to document classification, sequence labeling and sequence-to-sequence.
This means that doccano is more beginner-friendly (and probably in general more user-friendly) than brat, but contrary to brat one cannot define relationships or attributes. Depending on the choice of the use case, there are only labels on document level or span level.
The project type also determines the options for the annotation export format, which is either CSV or JSON -based.
doccano admits multiple users, but apart from that there are no additional features for collaborative labeling.
There are two extra features that you don't find in brat: You can write and save labeling guidelines right within the app (in Markdown ), and get a basic diagrammatic overview of the labeling stats.
The installation is easy and fully described on doccano's GitHub repo .
You don't need to understand what Docker is and does in order to install and use doccano. Just make sure Docker is installed .
There is not much to configure in doccano. You can create and edit labels directly in the browser UI, as well as labeling guidelines.
I recommend trying out doccano's live demos to get acquainted with its functionality.
INCEpTION is the follow-up project to WebAnno , which has received the highest overall score in the review mentioned above .
Like the first two tools, it uses a browser UI. It can be set up for a group of users on a server or as a standalone version.
INCEpTION is a way more heavyweight tool than either doccano or brat:
It can handle both text files and PDFs that contain text information (e.g. because they have been created from text files or by OCR software ), features an extensive "Settings" section that let's you configure virtually everything you can wish for,
provides functionality to facilitate collaborative labeling and evaluate the annotations statistically,
can export annotations in a broad range of common NLP labeling formats .
This being said, INCEpTION might be a bit overwhelming at first. I recommend that you feel free to ignore the features of INCEpTION you don't know what to do with and concentrate on what you need for your project.
INCEpTION comes with a comprehensive user guide describing in particular how to run it (see the "Getting started" section ).
Running INCEpTION is especially easy, because you can execute the downloaded JAR file without installing it.
Configuration and usage
There is so much to configure in INCEpTION that I cannot even really start to cover it here.
However, chances are that you are interested in INCEpTION because of its PDF labeling capacity, so I want to show you at least how to do that. In the following video I will
create a new project,
import a document,
define a label,
change the document viewer settings to display the document as a PDF file,
annotate the document.
To understand what else INCEpTION has to offer and how to use it, you really need to spend some time trying things out and reading the user guide.
You can start with the online demo version .
I have presented the three best free NLP labeling tools and pointed out how to use them.
To conclude, I will give you a coarse guideline how to choose the right tool for you among the three presented ones:
If you work with text files , what you want to do can be categorized as either document classification, sequence labeling or sequence-to-sequence and you don't need relations , but you want to start labeling as soon as possible without lengthy configurations, then choose doccano .
If your work with text files , want to keep things as simple as possible but need more functionality than provided by doccano, then try out brat .
If for some reason you want to work with (native) PDF files , or you are not afraid of a more complex annotation tool that takes some time get acquainted with but awards you with an extensive range of features , than INCEpTION is right for you.
If these labeling tools aren't enough to successfully realize your machine learning project and you need further consultation, feel free to book a Machine Learning Expert Talk with us.
August 30, 2022
The best free text labeling tools for text annotation and categorization in Natural Language Processing
What are the best free text labeling tools for text annotation in NLP?
Brat, INCEpTION and DACCANO have been described as the three best free text annotation tools that are suitable for manually labeling documents in Natural Learning Process (NLP) projects. This article will at length describe what this tool is suitable for, and then describe the installation and configuration process and its usage.
What is Text Annotation?
Text Annotation is the act of practicing and the result of adding a note to a text which may include comments, footnotes, highlights, and links. Text annotation can either be for private or shared reading. Its purpose is either collaborative writing and editing, commentary, reading or sharing. Text annotations help to train Natural Learning Process algorithms which require large annotate text datasets.
What is Text Categorization?
It is also known as text classification. Text classification ensures that annotators read a text or a group of texts. Text classification annotates an entire body or line of text with a single label.
BRAT is an online or virtual environment used for a combined text annotation that can be simultaneously run on a server and then used in a browser. It is used to annotate single expressions and the relationships in between. Hence, using BRAT to annotate longer text such as paragraphs is not convenient. Input documents must come as text files. It is often argued that the user interface (UI) presentation of the text file in brat is not like its original formatting. Due to these reasons, BRAT is not noted to be an ideal tool for annotating structured documents if you would prefer to directly annotate PDFs. The annotations are also stored in text files. BRAT has some major functionalities for collaborative labeling which are: multiple users are supported and there is an integrated annotation comparison.
DOCCANO is another annotation tool which is mainly for text files. It is believed to be easier and simpler to use than brat. Like BRAT, it runs server-based and has a browser user interface. However, it differs from BRAT in the sense that configuration of any kind is done in the web user interface. Its use case is limited to document classification, sequence labeling and sequence-to-sequence. Most importantly, DACANO is user-friendly as compared to BRAT and depending on the choice of the use case, labels are only on document level or span level. Based on the project type, you can determine the options for the annotation export format, which can either be CSV or JSON-based. Again, DOCCANO allows multiple users. However, unlike BRAT, DACCANO does not have additional features for collaborative labeling. Again, DOCCANO provides two extra features that are not available in BRAT. These features include writing and saving labeling guidelines right within the app (in Markdown) and getting a basic diagrammatic overview of the labeling stats.
INCEpTION is the follow-up project to WebAnno. Similarly, INCEpTION uses a browser user interface. It can be used in diverse ways either as a group of users on a server or as a standalone version. INCEpTION is noted to be a heavier tool than either DOCCANO or BRAT. It can be used for either text files or PDFs that contain text information. INCEpTION has an extensive feature that enables you to configure virtually everything. Again, it eases collaborative labeling and can statistically evaluate the annotations while exporting annotations in a broad range of common Natural Learning Process labeling formats. Nonetheless, INCEpTION can be complicated to use initially, and it has been advised to ignore the features that are complex to use. Due to its PDF labeling capacity, most people are drawn to using INCEpTION.
BRAT comes with detailed instructions on how to install it. If you just want to install and run brat on your local machine, then the standalone server is what you want. Firstly, you must place the data section of the instructions to learn how to set up the annotation files. As BRAT is not compatible with Python, you would have to modify the command python standalone.py to python2 standalone.py. BRAT is noted to work exceptionally well with Google Chrome.
DOCCANO is easier to use. When installing DOCCANO, you don’t necessarily need to understand what Docker is. This can be done provided Docker is installed. To get abreast with its functionality, try out doccano's live demos.
INCEpTION provides a comprehensive user guide that describes at length how to install and run it. Running INCEpTION is especially easy, because you can execute the downloaded JAR file without installing it.
Configuration & Usage
BRAT allows configuring a project-specific labeling scheme through .conf files. Using brat is fairly straightforward. Firstly, you must mark a text span which opens a pop-up menu. The options in the menu may depend on the configuration of the labeling scheme. However, it is necessarily easy to mark the exact desired span. Furthermore, if the marked span is too long, the pop-up menu may not fit on the screen.
INCEpTION demands a lot of configurations. INCEpTION has a lot of PDF labeling capacity. In creating a new project, you firstly must create a new project after which you must import a document. Then, you must define a label, change the document viewer settings to display the document as a PDF file and then annotate the document.
Unlike INCEpTION, DOCCANO does not demand too much configuration. DOCCANO allows you to create and edit labels directly in the browser user interface as well as labeling guidelines. To get abreast with DOCCANO’s functionality, it is recommended to try out DOCCANO’s live demos.
Top Paid Text Annotation Tools
Isahit offers a complete labeling solution developed specifically for text processing.
A unique service that combines customizable labeling tools, a dedicated project manager and a trained workforce for each of your needs. A platform designed and built along with data science teams in order to offer a solution that follows you in all stages of your natural language processing project.
- Named entity recognition tool
- Semantic annotation tool
- Text categorization tool
- Transcription tool
This is a text annotation tool that can be used to annotate text automatically or manually. Tagtog supports PDF annotation and includes pre-trained NER models for automatic text annotation.
Scale provides text annotation services such as text categorization, comparison, and OCR transcription. Scale provides computer vision and NLP data annotation services.
The LightTag text annotation tool is a platform for annotators and companies to label their text data in house.
This is a text annotation tool that helps to efficiently classify and annotate medical data. KConnect provides semantic annotation, text analysis, and semantic search services for medical information.
Access a trained Workforce, managed ethically.
Ethically scale your digital annotation projects with our highly trained workforce. Access our On-Demand Workforce to get the best quality in your Dataset Labeling.
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