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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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Latest papers without code

Knowledge Enhanced Contextual Word Representations

9 Sep 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

Semi-supervised Learning for Word Sense Disambiguation

26 Aug 2019

This work is a study of the impact of multiple aspects in a classic unsupervised word sense disambiguation algorithm.

WORD SENSE DISAMBIGUATION

GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge

20 Aug 2019

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

WORD SENSE DISAMBIGUATION

Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion

WS 2019

In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words.

WORD SENSE DISAMBIGUATION

Crowdsourced Hedge Term Disambiguation

WS 2019

We address the issue of acquiring quality annotations of hedging words and phrases, linguistic phenomenona in which words, sounds, or other constructions are used to express ambiguity or uncertainty.

WORD SENSE DISAMBIGUATION

Assessing Back-Translation as a Corpus Generation Strategy for non-English Tasks: A Study in Reading Comprehension and Word Sense Disambiguation

WS 2019

Corpora curated by experts have sustained Natural Language Processing mainly in English, but the expensiveness of corpora creation is a barrier for the development in further languages.

MACHINE TRANSLATION READING COMPREHENSION WORD SENSE DISAMBIGUATION

SenseFitting: Sense Level Semantic Specialization of Word Embeddings for Word Sense Disambiguation

30 Jul 2019

We outperform knowledge-based WSD methods by up to 25% F1-score and produce a new state-of-the-art on the German sense-annotated dataset WebCAGe.

WORD EMBEDDINGS WORD SENSE DISAMBIGUATION

Just ``OneSeC'' for Producing Multilingual Sense-Annotated Data

ACL 2019

The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages.

WORD SENSE DISAMBIGUATION