A guide to Text Annotation in Machine Learning

Modern technology is full of tools such as machine learning and artificial intelligence, but these technologies rarely receive attention. According to the 2020 State of AI and Machine Learning report, over 70% of companies use text for their AI solutions as the primary data source. The digital platform supports a variety of media types, including text, audio, images, and videos. Whether for personal or business purposes, the text is the most commonly used form of communication. Unstructured text data has accumulated over the years in organizations. What is the best way to utilize this text?

Assigning semantics, sentiments, or other characteristics to sentences in a text is the art and science of text annotation. Using it makes machines more intelligent because it helps them distinguish words in sentences. AI and ML applications can use this annotated text as training data.

An annotation, or label, is a way of assigning a name to a particular element of content in a text document. It is sometimes difficult for humans to understand and decode language, regardless of how far machine learning has progressed. To prepare datasets for training a model that can identify the language, context, and sentiment behind words, text annotation can highlight sentence components according to specific criteria.

The language employs idioms, metaphors, sarcasm, and rhetorical questions, as well as common expressions and colloquialisms. These idioms, metaphors, sarcasm, and rhetorical questions are culturally specific and require a thorough understanding of context to be understood correctly — something machines cannot do at this point. A common expression is “it’s as simple as a piece of cake!” Though it is intended to convey ease or simplicity, the NLP model of a machine may interpret it literally, as “it’s as simple as a piece of cake!” By analysing text annotations accurately, these AI models can better understand the data they are given, resulting in error-free interpretations.

Annotating text is the process of adding labels to digital files or documents as part of machine learning (ML). Annotations help prepare datasets that can be used to train machine-learning models for a variety of purposes due to the complexity of human language.

Many NLP technologies use machine learning techniques, including neural machine translation (NMT), auto question-and-answer platforms, smart chatbots, sentiment analysis, text-to-speech synthesizers, and automatic speech recognition (ASR). Many organizations across different industries can streamline their activities and transactions with these technologies.

There is usually highlighting or underlining of text, along with notes written in the margins, and in-text annotation datasets. We will cover the following types of text annotations:

People often respond sarcastically to questions. Our bad experiences with a restaurant or a hotel are usually shared on websites or reviews with sarcasm, which is easily misinterpreted by machines. In the case of machines learning sarcastic comments as compliments, this would cause the results to be skewed heavily. As a result, sentiment annotation becomes increasingly important. The technique qualifies every sentence as neutral, positive, or negative based on the emotion or attitude behind it (sarcasm here).

During annotation, annotators classify texts such as requests, commands, and confirmations based on the need or desire behind them.

Consider chatbot conversations, for instance. When users sign up, they often type things like “cancel my account,” “I want a refund,” “upgrade my services,” and “my order hasn’t arrived.” Artificial intelligence can’t identify your exact needs, let alone your motives.

When you ask a question, the machine learns what you’re asking and assesses your satisfaction level. By doing so, you can direct these inquiries to the appropriate answer on your FAQ page or direct them to the correct department within your company.

Annotating long text in this manner helps the machine learning model recognize key phrases, extract them, and tag them. An annotation of entities can also be classified based on their recognition as named entities (NER), key phrases, and points-of-speech (POS). Before feeding the data to a machine learning model, we often generate chatbot training datasets by using entity annotation.

Predefined category tags can be used to classify entire texts.

Labels are affixed to an entire body or line of text to categorize the text. A line of text or a block of text is given a category and a tag based on contextual data. Topics can be labelled, spam can be detected, and text messages and comments can be analysed for intent and emotion.

Content moderation, e-commerce, cataloguing, chatbots, web pages, and social media posts are a few of the use cases.

Annotating language data is similar to what we discussed so far, except that the data is translated into language. As a result, this technique involves two types of annotations: phonetic annotations and linguistic annotations, where intonations, natural pauses, and stress are also tagged.

Professional human annotators who are familiar with labelling text data can assist you in annotating the text. In addition to analysing and tagging different parts of the text, human annotators have expertise in tagging sentiments, intentions, and other characteristics of the text. The text annotation process has been sped up by using automated tools to create the data sets faster. Automatic tools assist annotators in labelling different parts of a speech or phrase automatically. In this step, annotations can be viewed and accepted or edited by annotators based on the suggested changes.

Despite its advantages, annotating data can be labour-intensive and time-consuming. It takes a lot of time and manual labour to do data annotation on your own, which is why most companies hire data annotation partners instead of doing it on their own.

There is no doubt that text annotation is the cherry on top of all annotation projects, no matter how difficult they might be. Models can read, comprehend, and act on information introduced through text annotation because of its variety of types and nascent use cases.