Text annotation for machine learning
Despite the massive shift towards digitization, some of the most complex layers of data are still stored in the form of text on papers or official documents. With the plethora of publicly available information, there comes the challenge of managing unstructured, raw data and making it understandable for the machines. Text is more complicated, unlike images and videos. 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 on text annotation by covering the fundamentals, as listed below:
What is text annotation?
Text annotation is the process of assigning labels to a text document or different elements of its content. 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 the emotion behind the words.
Why is it important?
Why do we 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. 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: NLP is no exception to that. Without human annotators, models won’t acquire the depth, nativity, often even the slang in which humans craft, control, and manipulate the language. That’s why companies continuously turn to human annotators to ensure sufficient amounts of quality training data. Current NLP-based AI solutions cover voice assistants, machine translators, smart chatbots, 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 image (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 elimination of manual data entry, error reduction, improved productivity, etc.
We’ll explore OCR and applications further as 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 a highlighted or underlined text, with notes around the margins. Here are the main text annotation types we’ll cover in this post:
Entity annotation
Entity annotation is used to label unstructured sentences with important information and is often applied in chatbot training datasets. 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 best suited to label key information from the text, be it people, geographic locations, frequently appeared objects or characters. NER is fundamental to NLP. Google Translate, Siri, and Grammarly are excellent examples of NLP that use NER to understand textual data.
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.
Keyphrase tagging: This one can be described as locating and labeling keywords or keyphrases in textual data.
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.
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 is different from NER in that NER spots the named entity in the text but does not specify which entity it is.
Text classification
While entity annotation refers to 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.
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 the SEO and boost the product’s visibility on the rankings page.
Sentiment annotation
Implied by the name, 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.
Use cases of text annotation
The use cases of text annotation are almost as all-around as those of image and video annotation. Roughly every discipline that contains textual data can be annotated and used for model training:
Healthcare
Text annotation is a game-changer in healthcare in that 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.
Insurance
- 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.
Similar to healthcare, text annotation has numerous benefits for the insurance industry.
Banking
- 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.
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:
Telecom
- 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.
- Detection of malicious activity, if any.
- Personalized promotion and product creation based on customer behavior analysis.
Las but not least, annotated text automates extensive human-powered work in the following areas:
Final thoughts
Text annotation does not cease to be the cherry on top all across the most complicated annotation projects. With the variety of types and nascent use cases, 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 CV pipeline? Don’t hesitate to reach out if you need more information or further assistance at any point throughout your pipeline.
Originally published at https://blog.superannotate.com.
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