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What is Text Annotation and its Types in Machine Learning?
Text annotation identifies and labels sentences with metadata to define characteristics of sentences. This could be highlighting parts of speech, grammar, phrases, keywords, emotions, and so on depending on the project. The better the quality and quantity of data, the better the model performs.
In this article, you will understand different text annotation methods.
1. Sentiment Annotation
Based on the emotion/sentiment associated with the response, the text is annotated. Sarcasm filled text should be understood as is, rather than being termed negative or positive. Sentiment is an important aspect here. Every sentence needs to be labelled based on the available options.
2. Intent Annotation
This tells about the intent of the user, i.e., when interacting with bots, users respond with different intentions. Some want to complain, discuss, ask for redemption, and so on. The different types of desires must be captured accurately by the models.
3. Linguistic Annotation
Linguistic annotation is a hybrid of the above discussed features, but this is done in a specific language. It involves phonetics annotation where intonations, natural pauses, stress, and other parts of the speech associated with the language are tagged too. The types include −
Part-of-speech − The annotation of the different function words within a text.
Phonetic Annotation − The labelling of intonation and natural pauses in speech.
Semantic Annotation − The annotation of word definitions.
4. Entity Annotation
This is the most important text annotation technique, which is used to identify, tag, and attribute multiple entities in each text or sentence. The types include −
Named Entity Recognition − The annotation of entities with proper names.
Keyphrase Ragging − The location and labelling of keywords or keyphrases in text.
Part-of-speech (POS) tagging − The annotation of the functional elements of speech adjectives, nouns, adverbs, etc.
5. Text Classification
It is also known as document classification or text categorization. In this method, annotators read paragraphs or sentences and understand the sentiments, emotions, and intentions behind them. These textual phrases are classified based on the comprehension specified by the respective project.
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