Significance of Text Annotation in Natural Language Processing

Through natural language processing (NLP), machines are able to slowly evolve into understanding the way humans converse, emote, understand, respond, analyze and copy human conversations as well as sentiment-driven behaviors. NLP is the key technology behind chatbots, text-to-speech tools, voice recognition, virtual assistants, etc.

In the pursuit of training machine learning modules into performing more complex tasks, there is a need for it to be trained with vast volumes of data using data annotation techniques. In case of NLP, the data annotation technique that’s applied is called text annotation. Text annotation is important as it ensures the machine learning model is able to understand and make inferences on the basis of information that it has been offered with. The two applications areas for text annotation are NLP and optical character recognition ( OCR).

Text Annotation in OCR

OCR involves extracting text data from scanned documents or images (JPG, PDF, etc.) into data that can be understood by models. It ensures that users are able to access information easily. It is of benefit to business operations and workflows as it saves time and resources which would otherwise be essential for managing unsearchable data. It also gets rid of manual data entry, reduction in errors, enhanced productivity, etc.

Types of Text Annotation in Natural Language Processing

1. Entity Annotation: This involves assigning entries in text alongwith predefined labels on the basis of their semantic meaning. The annotated text is thereby given to machine learning models to extract the hidden meaning in text data entries. It involves identification, extraction, and tagging of entities in text by using the techniques highlighted below.

i. Named entity recognition (NER): This is used for labeling vital information from text based on people, geographical locations, objects or characters appearing frequently.

ii. Part-of-speech tagging: This involves parsing of sentences and identification of units including nouns, verbs, adjectives, pronouns, adverbs, prepositions, conjunctions, etc. This is important as it helps in identifying various parts of speeches. For example, the word ‘book’ may mean the noun version in “I read this book.” The verb version i.e. ‘I will book the tickets’.

iii. Keyphrase tagging: This involves identifying and labeling keywords in text data. It is helpful in cases where there is a lengthy document and one needs to quickly get an idea of the key concepts discussed in the text without going through the entire document.

2. Entity Linking: This involves mapping of words in a given text to entities in a knowledge base. While entity annotation involves locating or extracting entities in a text, entity linking involves connecting the named entities to larger datasets.
3. Sentiment Annotation: This is used to determine the emotion or opinion in a given text. It involves a close analyses of text, selecting the label which ideally represents the emotion, sentiment or opinion. It assists businesses in developing strategies on the way products or services are placed in the market and the way to track it further.

Use Cases for Text Annotation

1. Medical Industry: Medical literature can be annotated with terms relating to ailments, treatments, etc. for creating datasets. This helps with knowledge discovery and information extraction.
2. Financial Industry: Financial documents are annotated for extracting key information relating to risk assessment and decision-making. Market sentiments are then measured using sentiment analysis of news stories, social media posts and financial reports.
3. E-commerce: It is used in e-commerce to extract product attributes, carry out customer sentiment analysis and categorizing products. It assists in understanding market trends, product preferences as well as consumer feedback.
4. Customer Service: It is used by businesses for classifying and examining email correspondence, chats, customer support tickets, etc. to hasten time to response and identify problems from recurring.
5. Legal: It is used in the legal domain for categorizing and extracting data from contracts, case laws and legal documents for carrying out legal research and compliance.
6. Marketing and Social Media: It is used in social media for creating user profiles, sentiment analysis and classifying content. It is also used by marketing professionals for carrying out targeted campaigns, assessing sentiments of consumers, and understanding opinions of customers.

To sum it up, the complexity of projects have only added to the complexity of text data sourcing and labeling. Hence, its critical to liaise with data annotation experts for obtaining the most accurate AI training data for your modules.