Text Annotation
Natural language processing (NLP) helps machines understand text or speech from humans. NLP provides various applications in chatbots, automatic speech recognition, and sentiment analysis programs that can improve efficiency and productivity in various sectors around the world. In supervised learning, computer scientists not only need to prepare structured data but also require well-labeled data for machines to identify key information. Five types of text annotation can be useful during the NLP data preparation process.
1. Entity Annotation
Entity annotation is one of the most important processes in the generation of chatbot training datasets and other NLP training data. It involves locating, extracting, and labeling entities in text. (Example)
2. Entity Linking
Entity annotation is about locating and annotating certain entities within the text, while entity linking is the process of connecting those entities to larger repositories of data about them.
3. Text Classification
Also known as text categorization or document classification, text classification tasks annotators with reading a body of text or short lines of text. Annotators must analyze the content, discern the subject, intent, and sentiment behind it, and classify it based on a predetermined list of categories. Entity annotation is to label individual words or phrases, while text classification is the process of annotating an entire body or line of text with a single label.
4. Sentiment Annotation
Emotional intelligence is one of the trickiest fields of machine learning. Sometimes it is difficult even for humans to guess the true emotion behind a text message or email. It is more difficult for a machine to determine the connotations hidden in the text that uses sarcasm, wit, or other casual forms of communication. To help machine learning models understand the sentiment behind the text, the models are trained with sentiment-annotated text data.
5. Linguistic Annotation
Also referred to as corpus annotation, linguistic annotation simply describes the process of tagging language data in text or audio recordings. With linguistic annotation, annotators are tasked with identifying and flagging grammatical, semantic, or phonetic elements in the text or audio data.
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