dashanant/wordsense

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

Feel free to drop your suggestion at the following email address:

• Author: Anant Dashpute
• Email: [email protected]