Natural language text dialogue is one of the main challenges in the semantic understanding of network big data, known as the jewel in the crown of artificial intelligence, and text data annotation is the most basic and important link in this series of work. So, what is text annotation? What are the labeling methods? Let’s introduce it below.

What are text annotations?

Text annotation is a supervised learning problem, mainly used in natural language processing. Text labeling is the process of feature-marking text, labeling it with specific semantics, composition, context, purpose, emotion, and other original data labels. Through labeled training data, we can teach machines how to recognize hidden texts. Containing intentions or emotions, so that machines can understand language more humanely.

Therefore, we must complete high-quality text data comprehensively and accurately to ensure that the machine can accurately recognize human intentions. If the text is not processed properly, the machine will not be able to understand the content we marked.

The method of text annotation:

1. Sequence labeling: Sequence labeling covers a very wide range, including word segmentation, entities, keywords, prosody, and intent understanding. It is the most basic task of natural language processing tasks.

2. Relational labeling: An important task of labeling the syntactic and semantic relationships of complex sentences is the formal labeling of complex sentence automatic analysis. Relation annotations include: pointing relationship, modification relationship, parallel corpus, etc.

3. Attribute annotation: label the attributes of things, attribute annotation includes: text category, news, entertainment, etc.;

4. Category labeling: label the categories of articles, such as chapter-level reading comprehension, etc.

Application type of text annotation:

1. Semantic recognition

Semantic recognition is to use the platform to mark the text, the same content, different segmentation, different order, the meaning of expression will be completely different, so if you want the computer to clearly recognize, the first step is to tell the computer, in each In a sentence, which words are a phrase, this is the process of word segmentation, and Chinese has a very strong ambiguity, so accurate word segmentation is very complicated and challenging.

2. Emotion recognition

Emotion recognition originally refers to the automatic identification of an individual’s emotional state by AI by obtaining individual physiological or non-physiological signals, which is an important part of emotional computing. The content of emotion recognition research includes facial expression, voice, behavior, heart rate and text, etc., and the user’s emotional state can be judged through the above content.

3. Entity recognition

An information extraction technique. Obtain entity data such as person names and place names from text data.

4. Data cleaning

Data cleaning refers to the last procedure for discovering and correcting identifiable errors in data files, including checking data consistency, dealing with invalid and missing values, etc. Data cleaning after input is generally done by a computer.