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
What is text annotation?
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.
Why is it important?
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.
How is text annotated: NLP text annotation
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.
Text annotation for OCR
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.
Types of 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.
"SuperAnnotate was among the top 100 AI companies, and top 3 annotation companies according to CB insights in 2021"
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.
Use cases of text annotation
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.