Annotation is the process of labeling data for the purpose of training machine learning algorithms. Annotation is a key component of the Unstructured Information Management Architecture (UIMA). UIMA is a platform-independent architecture for processing unstructured data, such as text, audio, and video. The UIMA architecture defines a set of services and components that can be used to build applications that analyze unstructured data.
Annotation is a key component of Natural Language Processing (NLP). NLP is a branch of artificial intelligence that deals with the interpretation and manipulation of human language. NLP algorithms are used to process and analyze text, speech, and other forms of natural language. Annotation is used to train NLP algorithms so that they can accurately interpret and manipulate human language.
Annotation can also refer to the process of labeling data for the purposes of classification or indexing. For example, audio annotation may refer to the process of labeling audio files with metadata such as speaker, gender, and emotion. This type of annotation is typically done manually by humans. Audio annotation is a key component of speech recognition systems.
Annotation can also refer to the process of labeling video data with metadata such as objects, people, and events. This type of annotation is typically done manually by humans. Video annotation is a key component of video analysis systems.
Text Annotation and its Different Types
Text annotation and AI development go hand in hand because this is how machines learn from texts. Different types of text annotation are:
- Entity Annotation. Entity Annotation has three types: identifying parts of speech, keyphrase tagging, and part-of-speech tagging (POS).
- Identifying parts of speech is identifying proper nouns, in a text. This is also sometimes called named entity recognition (NER).
- keyphrase tagging involves labeling short phrases or terms that are relevant to a particular topic.
- part-of-speech tagging (POS), is the process of labeling words in a text as verbs, nouns, adjectives, etc.
2.Entity linking. Entity linking includes two processes: end-to-end entity linking and entity disambiguation. Entity linking is useful for better search functions by mapping ambiguous terms to their corresponding entities. This can be useful, for example, when search engines index unstructured data sources such as blog posts or social media posts.
- End-to-end entity linking is the process of automatically linking entities in a text to their corresponding entries in a knowledge base, such as Wikipedia.
- Entity disambiguation is the process of selecting the correct sense or meaning of an entity from a set of possible candidates.
3.Text classification. Text classification includes document classification, product categorization, and sentiment annotation.
- Document classification is the process of assigning a document to one or more predefined categories.
- Product categorization is the process of assigning a product to one or more predefined categories.
- Sentiment annotation is the process of labeling a text as positive, negative, or neutral.
4.Linguistic annotation. Linguistic annotation includes discourse annotation, part-of-speech (POS) tagging, phonetic annotation, and semantic annotation.
- Discourse annotation is the process of labeling the structure of a text at the discourse level.
- Part-of-speech (POS) tagging is the process of labeling words in a text as verbs, nouns, adjectives, etc.
- Phonetic annotation is the process of labeling the phonetic pronunciation of words in a text.
- Semantic annotation is the process of labeling the meaning of words or phrases in a text.