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Word Sense Disambiguation

37 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

An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages

LREC 2018 nlpub/watasense

The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context.

SEMANTIC TEXTUAL SIMILARITY WORD SENSE DISAMBIGUATION

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

EMNLP 2017 uhh-lt/wsd

In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.

WORD SENSE DISAMBIGUATION

Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts

WS 2016 clips/yarn

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

WORD SENSE DISAMBIGUATION

Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings

NAACL 2016 spandanagella/verse

We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i. e., the one that describes the action depicted in the image.

IMAGE RETRIEVAL 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

LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)

WS 2019 danlou/LMMS

This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5.

WORD SENSE DISAMBIGUATION

Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction

ACL 2018 pvthuy/part-whole-relations

This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction.

RELATION EXTRACTION WORD SENSE DISAMBIGUATION