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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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Latest papers without code

Sparse associative memory based on contextual code learning for disambiguating word senses

14 Nov 2019

In recent literature, contextual pretrained Language Models (LMs) demonstrated their potential in generalizing the knowledge to several Natural Language Processing (NLP) tasks including supervised Word Sense Disambiguation (WSD), a challenging problem in the field of Natural Language Understanding (NLU).

LANGUAGE MODELLING WORD SENSE DISAMBIGUATION

Word Sense Disambiguation using Knowledge-based Word Similarity

11 Nov 2019

Furthermore, our system outperformed the existing knowledge-based WSD systems and showed a performance comparable to that of the state-of-the-art supervised WSD systems.

WORD SENSE DISAMBIGUATION

Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings

5 Nov 2019

We hypothesize that not all dimensions are equally important for downstream tasks so that our algorithm can detect unessential dimensions and discard them without hurting the performance.

WORD EMBEDDINGS WORD SENSE DISAMBIGUATION

Low-dimensional Semantic Space: from Text to Word Embedding

3 Nov 2019

This article focuses on the study of Word Embedding, a feature-learning technique in Natural Language Processing that maps words or phrases to low-dimensional vectors.

WORD SENSE DISAMBIGUATION

Knowledge Enhanced Contextual Word Representations

IJCNLP 2019

Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities.

ENTITY EMBEDDINGS ENTITY LINKING ENTITY TYPING LANGUAGE MODELLING WORD SENSE DISAMBIGUATION

Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation

IJCNLP 2019

They represent ambiguous words as the players of a non cooperative game and their senses as the strategies that the players can select in order to play the games.

WORD SENSE DISAMBIGUATION

GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge

IJCNLP 2019

Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context.

WORD SENSE DISAMBIGUATION

SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations

IJCNLP 2019

Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed.

WORD SENSE DISAMBIGUATION

Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations

IJCNLP 2019

Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis.

NAMED ENTITY RECOGNITION QUESTION ANSWERING SENTIMENT ANALYSIS WORD EMBEDDINGS WORD SENSE DISAMBIGUATION

Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations

IJCNLP 2019

Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis.

NAMED ENTITY RECOGNITION QUESTION ANSWERING SENTIMENT ANALYSIS WORD EMBEDDINGS WORD SENSE DISAMBIGUATION