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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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Latest papers without code

Making Fast Graph-based Algorithms with Graph Metric Embeddings

17 Jun 2019

The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs.

SEMANTIC TEXTUAL SIMILARITY WORD SENSE DISAMBIGUATION

Learning Graph Embeddings from WordNet-based Similarity Measures

SEMEVAL 2019

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

L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

SEMEVAL 2019

We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments.

GRAPH CLUSTERING WORD SENSE DISAMBIGUATION

In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse

28 Mar 2019

We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse.

NAMED ENTITY RECOGNITION WORD SENSE DISAMBIGUATION

Polylingual Wordnet

4 Mar 2019

The translation of wordnets is fundamentally complex because of the need to translate all senses of a word including low frequency senses, which is very challenging for current machine translation approaches.

MACHINE TRANSLATION WORD SENSE DISAMBIGUATION

Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation

23 Feb 2019

In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem.

LANGUAGE MODELLING WORD SENSE DISAMBIGUATION

Context based Analysis of Lexical Semantics for Hindi Language

23 Jan 2019

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context.

WORD SENSE DISAMBIGUATION

Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships

2 Nov 2018

Our method leads to state of the art results on most WSD evaluation tasks, while improving the coverage of supervised systems, reducing the training time and the size of the models, without additional training data.

WORD SENSE DISAMBIGUATION

Local Homology of Word Embeddings

24 Oct 2018

Topological data analysis (TDA) has been widely used to make progress on a number of problems.

TOPOLOGICAL DATA ANALYSIS WORD EMBEDDINGS WORD SENSE DISAMBIGUATION