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Word Sense Disambiguation Edit

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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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.

To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation

6 Sep 2019

Then, these models were evaluated on the word sense disambiguation task.

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.

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.

Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion

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

Crowdsourced Hedge Term Disambiguation

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.

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

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.

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.

Just OneSeC'' for Producing Multilingual Sense-Annotated Data

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.

Making Fast Graph-based Algorithms with Graph Metric Embeddings

Graph measures, such as node distances, are inefficient to compute.