Word Sense Disambiguation

107 papers with code • 13 benchmarks • 16 datasets

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).


Use these libraries to find Word Sense Disambiguation models and implementations
4 papers

Most implemented papers

Language Models are Few-Shot Learners

openai/gpt-3 NeurIPS 2020

By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

microsoft/DeBERTa ICLR 2021

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.

FlauBERT: Unsupervised Language Model Pre-training for French

getalp/Flaubert LREC 2020

Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.

An Incremental Parser for Abstract Meaning Representation

mdtux89/amr-evaluation EACL 2017

We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.

GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge


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

Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts

clips/yarn WS 2016

In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text.

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

getalp/disambiguate GWC 2019

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-Class Embeddings for Multiclass Text Classification

AlexMoreo/word-class-embeddings 26 Nov 2019

Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few.

Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

Nithin-Holla/MetaWSD Findings of the Association for Computational Linguistics 2020

Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.