Ever been stunned by how your smartphone seems to accurately predict what you have in mind as you type your text responses? Or, have you ever been in awe of how you got your questions answered or money refunded by a customer service associate who was not even a human after all? Well, behind every such surprising incident, there are concepts in action like Artificial Intelligence, Machine Learning, and most importantly, NLP (Natural Language Processing). One of the biggest breakthroughs of our recent times is NLP, where machines are gradually evolving to understand how humans talk, emote, comprehend, respond, analyze, and even mimic human conversations and sentiment-driven behaviors. This concept has been highly influential in the development of chatbots, text-to-speech tools, voice recognition, virtual assistants, and more.
If Alexa or Siri could come back with quirky responses to our bizarre questions, that’s because NLP and its allied technologies like artificial intelligence and machine learning have evolved to an extent that they could almost crack the Turing Test. However, reaching here wasn’t easy, and going forward won’t be, either. To push the boundaries, we need to train machine learning modules with more and more volumes of data and this can happen only with proper data annotation techniques. For the uninitiated, data annotation is the process of labeling data with descriptions or information to make it understandable by machines. As far as NLP is concerned, the data annotation technique we apply is called text annotation. Let’s explore this a little more.
What is Text Annotation?
Text annotation is identifying and labeling sentences with additional information or metadata to define the characteristics of sentences. This information could be highlighting parts of speech in a sentence, grammar syntax, keywords, phrases, emotions, sarcasm, sentiments and more depending on the scope of a project. Machine learning modules are fed with such AI training data, where they learn diverse aspects of sentences, sentence formation, and more to understand human conversations better. As they learn with properly annotated data, they become better at mimicking human conversations (current virtual assistants). However, feed them with poorly annotated data, and you will find them deliver irrelevant, dumb, or misleading responses. That’s why text labeling should be done by experts, who meticulously tag every single aspect of a sentence to ensure nothing crucial for machines to understand and learn is overlooked. To achieve precision, experts deploy distinct text annotation techniques. What are they? Let’s find out.
Types of Text Annotation Techniques
- Sentiment Annotation: Often, humans tend to be sarcastic in their responses. Especially on websites and reviews, we tend to share our bad experiences with a restaurant or a hotel through sarcasm and machines could easily misinterpret them as compliments. If every sarcastic comment is learned as a compliment by machines, this would completely skew the results. That’s why sentiment annotation becomes crucial. This technique specifies the emotion or attitude behind a sentence (sarcasm in this case) and every sentence is labelled as neutral, positive, or negative.
- Intent Annotation: This technique differentiates the intentions of users. When interacting with chatbots, different users respond with different intentions. Some request statements, others command responses for overcharges, a few confirm the debit of money, and more. These distinct types of desires are classified through appropriate labels in this technique.
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Entity Annotation: This is the most important text annotation technique, which is used to identify, tag, and attribute multiple entities in a given text or sentence. We could break down entity annotation further into the following:
- Keyphrase tagging – this involves locating and identifying keywords in a text.
- Named Entity Recognition – this involves annotating proper names such as names of people, places, countries, and more.
- Parts Of Speech Annotation – this involves identifying nouns, verbs, adjectives, punctuations, prepositions, and more in a sentence.
- Text Classification: Otherwise, known as document classification or text categorization, annotators read chunks of paragraphs or sentences and understand the sentiments, emotions, and intentions behind them. They then classify the text based on their comprehension into categories specified by their projects. It could be as simple as classifying a piece of the article under entertainment or sports or as complex as categorizing products in an eCommerce store.
- Linguistic Annotation: Linguistic annotation involves a bit of everything we discussed so far but the only difference here is that the annotation process is done on language data. Because of this, this technique involves an additional type of annotation type called phonetics annotation, where intonations, natural pauses, stress, and more are tagged as well.
Text Annotation Use Cases
Text annotation is used in a variety of industries and sectors where natural language processing (NLP) and machine learning are used. Here are a few industries where text annotation is commonly used:
Medical Research and Healthcare:
- Annotators may annotate text in medical literature with terms related to illnesses, ailments, and treatments in order to create datasets for knowledge discovery and information extraction.
Finance:
- Financial institutions measure market sentiment by using text annotation for sentiment analysis of news stories, social media posts, and financial reports.
- Financial documents are annotated to extract pertinent information for risk assessment and decision-making.
Retail and E-commerce:
- Text annotation is used in e-commerce to extract product attributes, analyse customer sentiment from reviews, and categorize products.
- It aids in comprehending trends, product preferences, and customer feedback.
Customer service and support:
- Businesses classify and examine email correspondence, chat transcripts, and customer support tickets using text annotation to speed up response times and spot recurring problems.
Legal and Compliance:
- Text annotation is used in the legal field to categorise and extract data for legal research and compliance from contracts, case law, and legal documents.
Marketing and Social Media:
- Text annotation is used by social media platforms for user profiling, sentiment analysis, and content classification.
- Marketing teams use annotated data to run targeted campaigns, assess consumer sentiment, and understand customer opinions.
Data Extraction and Search Engine Optimisation:
- By comprehending the purpose and context of user queries, search engines employ text annotation to enhance search results.
- Search engine algorithms benefit from the structured data created by annotating web pages.
Human Resources:
- Text annotation is used in recruitment to match candidates with job requirements by analysing resumes, cover letters, and job descriptions.
- Performance evaluations and employee comments are also annotated for sentiment analysis.
Academic Research:
- Scholars employ text annotation techniques to classify and examine scholarly articles, journals, and papers in order to conduct literature reviews and retrieve relevant information.
Public Services and Government:
- Government agencies use text annotation to analyse public opinion, classify citizen feedback, and extract data from documents.
Conclusion
So, these were the different types of text annotation techniques. We believe you now have a better idea of how even simple applications of NLP perform so accurately on our smartphones. As projects become more complex, text data sourcing and labeling become equally complex as well. That’s why it is important to collaborate with data annotation experts to get the most precise AI training data for your modules.
Frequently Asked Questions (FAQs)
Q. What is text annotation and labeling?
The process of adding metadata or labels to unstructured text data is known as text annotation and labelling. This helps with natural language processing (NLP) and machine learning tasks by making the text more machine-readable and structured.
Q. What makes text annotation significant?
In NLP tasks, text annotation is essential for training machine learning models. By linking distinct characteristics or categories to various textual segments, it facilitates the understanding and learning process of algorithms.
Q. What kinds of text annotations are most common?
Text classification, named entity recognition (NER), sentiment analysis, part-of-speech tagging, event extraction, and relation extraction are examples of common types text annotations.
Q. What is the connection between text annotation and supervised learning?
Annotated text data is used in supervised learning to train machine learning models. In order to predict outcomes for newly uncovered data, models acquire patterns from labelled examples.