Abstract
This paper discuss various technique of word sense disambiguation. In WSD we disambiguate the correct sense of target word present in the text. WSD is a challenging field in the natural language processing, it helps in information retrieval, information extraction, machine learning. There are two approaches for WSD machine learning approach and knowledge based approach. In Knowledge based approach a external resource is used to help in disambiguation process, but in Machine learning approach a corpus is used whether it is annotated, un-annotated or both.
Highlights
Almost in all human languages there are words, which have different own separate sense in different context
Word sense disambiguation is important machine Tranlation(ML), Semantic mapping(SM), ontology Learning(OL), Semantic Annotation(SA), it helps in Information Retrieval(IR), Information Extraction(IE)
We the human being are with intelligence, we can get the proper sense of the polysemous word in particular context
Summary
Almost in all human languages there are words, which have different own separate sense in different context. This is a important approach in natural language processing(NLP). We the human being are with intelligence, we can get the proper sense of the polysemous word in particular context. We can do it with the help of surrounding words i.e. the local or global context. After the system can disambiguate the proper sense of the ambiguous word in the particular context. With the machine learning we can only process the sentence and if any polysemous word occurs the surrounding of the ambiguous context of the target word is analyzed.
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