Word Sense Disambiguation using Bidirectional LSTM
| dc.contributor.author | Rakshith, J. | |
| dc.contributor.author | Savasere, S. | |
| dc.contributor.author | Ramachandran, A. | |
| dc.contributor.author | Akhila, P. | |
| dc.contributor.author | Koolagudi, S.G. | |
| dc.date.accessioned | 2026-02-06T06:37:19Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Word Sense Disambiguation is considered one of the challenging problems in natural language processing(NLP). LSTM-based Word Sense Disambiguation techniques have been shown effective through experiments. Models have been proposed before that employed LSTM to achieve state-of-the-art results. This paper presents an implementation and analysis of a Bidirectional LSTM model using openly available datasets (Semcor, MASC, SensEval-2 and SensEval-3) and knowledge base (WordNet). Our experiments showed that a similar state of the art results could be obtained with much less data or without external resources like knowledge graphs and parts of speech tagging. © 2019 IEEE. | |
| dc.identifier.citation | 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2019 - Proceedings, 2019, Vol., , p. - | |
| dc.identifier.uri | https://doi.org/10.1109/DISCOVER47552.2019.9008031 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/31003 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Bidirectional LSTM | |
| dc.subject | Word Sense Disambiguation | |
| dc.subject | WordNet | |
| dc.title | Word Sense Disambiguation using Bidirectional LSTM |