Word Sense Disambiguation - Supervised Approaches: Present Scenario
Aparitosh Gahankari, Dr. Avanish S.Kapse, V. M. Thakre · Zenodo (CERN European Organization for Nuclear Research) · 2020
This paper covers the discussion of how a meaningful sense of the given word can be selected in the given context. In the domain of Natural Language Processing (NLP), Word Sense Disambiguation (WSD) is still an open problem. The use of WSD can be there in many fields including but not limited to Machine Translation, Text Pre Processing, Information Retrival, etc. To deal with Problem of correctly identification of the sense different approaches are used specifically in the Machine Learning. This paper is a kind of Survey wherein we will present the current scenario of the different Machine Learning (ML) Algorithms & the techniques used with the particular data set the said algorithm is applied on. This paper should be helpful to those who are novice in the NLP, specifically from WSD domain point of view. This survey concludes that there are some ML algorithms which works efficiently on some data sets while the others works best on data set from different languages.