Sliding window based part-of-speech tagging is used to part-of-speech tag a text.
A high percentage of words in a natural language are words which out of context can be assigned more than one part of speech. The percentage of these ambiguous words is typically around 30%, although it depends greatly on the language. Solving this problem is very important in many areas of natural language processing. For example in machine translation changing the part-of-speech of a word can dramatically change its translation.
Sliding window based part-of-speech taggers are programs which assign a single part-of-speech to a given lexical form of a word, by looking at a fixed sized "window" of words around the word to be disambiguated.
The two main advantages of this approach are:
- It is possible to automatically train the tagger, getting rid of the need of manually tagging a corpus.
- The tagger can be implemented as a finite state automaton (Mealy machine)
Read more about Sliding Window Based Part-of-speech Tagging: Formal Definition, Further Reading
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