Natural language processing is a difficult task and the pure nature of complexity of the english language adds to this. Therefore, in this field we solve many subtasks to get closer to an interpretation that is truly natural. One such subtask is word sense disambiguation or WSD for short. This article explores this problem and delineates a new approach to solving it based on the recent outstanding paper in ACL 2019 by Kumar, Jat et al: Zero-shot Word Sense Disambiguation using Sense Definition Embeddings.
What is word sense disambiguation?
Words have different meanings and different intentions based on the context they are used in, even if they are the same words. These “difficult” words are more generally referred to as homonyms. Take the following example:
He was not feeling well after the marathon.
The farmer grabbed the pail from the well.
Both sentences use the word “well,” but they clearly have different senses in each setting.
Word sense disambiguation (WSD) denotes the task of associating a word to its proper definition based on context clues.
RELATED WORK — What approaches exist to solve this problem?
WSD is a well explored problem and many approaches exist to tackle it.
The first are supervised and semi-supervised approaches that treat the definitions as discrete labels. One such supervised approach is context2vec that also uses Bidirectional LSTMs to learn generic context embedding functions. This and other supervised models fail to do well for words that are not included in the training dataset, so unseen and uncommon words cannot be deciphered.
The other type of approach is unsupervised knowledge based. One such unsupervised approach is Enhanced Lesk WSD, which uses WordNet to determine the number of shared words between the context of a word and each definition via a word similarity function. However, maximum overlap heuristics don’t perform as well as supervised approaches.
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