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      A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation

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          Abstract

          Word sense disambiguation (WSD) is a well researched problem in computational linguistics. Different research works have approached this problem in different ways. Some state of the art results that have been achieved for this problem are by supervised models in terms of accuracy, but they often fall behind flexible knowledge-based solutions which use engineered features as well as human annotators to disambiguate every target word. This work focuses on bridging this gap using neural sequence models incorporating the well-known attention mechanism. The main gist of our work is to combine multiple attentions on different linguistic features through weights and to provide a unified framework for doing this. This weighted attention allows the model to easily disambiguate the sense of an ambiguous word by attending over a suitable portion of a sentence. Our extensive experiments show that multiple attention enables a more versatile encoder-decoder model leading to state of the art results.

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          Most cited references 6

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          context2vec: Learning Generic Context Embedding with Bidirectional LSTM

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            Neural Sequence Learning Models for Word Sense Disambiguation

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              SemEval-2007 task 07

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                Author and article information

                Journal
                04 September 2018
                Article
                1809.01074

                http://creativecommons.org/licenses/by-nc-sa/4.0/

                Custom metadata
                9 pages, 3 Figures, Accepted as a conference paper in ICMLA 2018
                cs.CL cs.LG

                Theoretical computer science, Artificial intelligence

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