Wordsense

The whole idea of word sense is controversial. The meaning of a word is highly contextual and depends on its usage in sentence.

English is very difficult language to learn by robot as a lot of words are Ambiguous( Word with diffrent meanings).

To solve this state-of-the-art problem, we have implemented the solution using Knowledge-Based Method , which concerned with identifying which sense of a word is used in a sentence.

How to Install

$ python
>>>pip install -U nltk

>>>nltk.download('stopwords')

>>>pip install Get_Wordsense

Demonstration

$ python

>>>sentence = 'I went to the bank to deposit my money'

>>>ambiguous_word = 'bank'

>>>print Get_sense(sentence, ambiguous_word, pos=None, synsets=None)

'a financial institution that accepts deposits and channels the money into lending activities'

Cite

To cite Get_Wordsense:

Anant Dashpute. 2021. Get_Wordsense: Python Implementation of Get_Wordsense. Retrieved from https://github.com/DASHANANT/Get_Wordsense

References

• Michael Lesk. 1986. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In Proceedings of the 5th annual international conference on Systems documentation (SIGDOC '86), Virginia DeBuys (Ed.). ACM, New York, NY, USA, 24-26. DOI=10.1145/318723.318728 http://doi.acm.org/10.1145/318723.318728

• Zhi Zhong and Hwee Tou Ng. 2010. It makes sense: a wide-coverage word sense disambiguation system for free text. In Proceedings of the ACL 2010 System Demonstrations. Association for Computational Linguistics, USA, 78–83.

Suggestions

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• Author: Anant Dashpute
• Email: anantdashpute@gmail.com