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Word Sense Disambiguation

38 papers with code · Natural Language Processing

The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet. For example, given the word “mouse” and the following sentence:

“A mouse consists of an object held in one's hand, with one or more buttons.”

we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).

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Greatest papers with code

Incorporating Glosses into Neural Word Sense Disambiguation

ACL 2018 jimiyulu/WSD_MemNN

GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.

WORD SENSE DISAMBIGUATION

Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

ACL 2019 malllabiisc/EWISE

To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.

KNOWLEDGE GRAPH EMBEDDING WORD SENSE DISAMBIGUATION ZERO-SHOT LEARNING

Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation

14 May 2019getalp/disambiguate

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database.

WORD SENSE DISAMBIGUATION

SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation

EMNLP 2017 SI3P/SupWSD

In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD).

WORD SENSE DISAMBIGUATION

Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation

ACL 2019 danlou/LMMS

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings.

LANGUAGE MODELLING WORD EMBEDDINGS WORD SENSE DISAMBIGUATION