Linguistic annotation is also termed corpus annotation and it involves the process wherein language data is tagged in audio recordings or in text. The annotators are assigned a task to identify and flag phonetic, semantic and grammatical elements in the audio or text data. The global experts are fluent in innumerable languages and dialects to perform their linguistic annotation tasks efficiently. Here are some of the linguistic annotation types:
- (POS)Part of Speech tagging: In this type of annotation, the distinct function words are annotated within a text.
- Discourse Annotation: Here the linking of cataphors and anaphors to their postcedent or antecedent subjects take place. Example: John broke the table. He felt terrible about it.
- Semantic Annotation: The word definitions are annotated here.
- Phonetic Annotation: The natural pauses, stress and intonation in speech are labelled here.
Linguistic annotation helps in creating AI training datasets for a wide range of NLP solutions like search engines, virtual assistants, machine translation, chatbots and more. Social norms and the human background that is inclusive of the norms keep changing from time to time. Therefore, humans will need to cover the human experiences and views across all dialects, languages and cultures consistently. Hence, linguistic annotation is highly popular and in-demand when it comes to text annotation.