What does Data Labeling or Data Annotation mean?
According to many different studies, data annotation (POS labeling, PoS labeling, or POST Part-of-speech Tagging) are generally known as grammar detection, data tagging. They are aiming to tag and separate different areas of a text according to the definition of it. For a popular example of data labeling in academy program, at kinder gardens, we often see some teaching program of defining nouns, verbs, adverb,… in a text.
After the manual data tagging, the POS labeling is applied under an intelligent and automatic language system, using different methods to identify and segment the targeted areas, as well as different forms of data and texts according to the datasets (Siddesh, G. M., et al, 2020). Those algorithm methods of POS labeling are known as two popular groups: Predictable group and Random group.
There are some principles for data labeling
In general, labeling or tagging a word according to its function is more complicated and time-consuming than identify the function of the words according to available listing words. It means that one word may have different meaning and function according to purpose of the users or the sentence. Therefore, the AI or machine usually find it is difficult to identify the actual function of the word. For an instance of popular using complicated tagging to a word, there are many mistakes or misunderstanding about how to labeling a word in a high accuracy and suitable area.
For example: Can we table (verb) this table (noun) problem on another day?
BPO.MP Data Annotation Service
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