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Word Sense Disambiguation

30 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

Incorporating Glosses into Neural Word Sense Disambiguation

ACL 2018 jimiyulu/WSD_MemNN

GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.

WORD SENSE DISAMBIGUATION

Cross-lingual Lexical Sememe Prediction

EMNLP 2018 thunlp/CL-SP

We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction.

SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION

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

SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation

EMNLP 2017 SI3P/SupWSD

In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD).

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

Learning Graph Embeddings from WordNet-based Similarity Measures

SEMEVAL 2019 uhh-lt/path2vec

We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities.

GRAPH EMBEDDING SEMANTIC TEXTUAL SIMILARITY WORD SENSE DISAMBIGUATION