The Role of WSD for Multilingual Natural Language Applications

Montoyo, Andrés; Romero, Rafael; Vázquez, Sonia; Calle, Ma Carmen; Soler, Susana Pérez

doi:10.1007/3-540-46154-x_6

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The role of WSD for multilingual scenario of NLP text was surveyed using English-Spanish languages [18]. WSD in multilingual machine translation (MT) is based on the concept that resource full language helps a resource low language by projecting parameters like sense distributions, and corpus cooccurrences [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Wiki sense bag creation using multilingual word sense disambiguation

Patankar

Phadke

Devane

2022

IJ-AI

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Performance of word sense disambiguation (WSD) is one of the challenging tasks in the area of natural language processing (NLP). Generation of sense annotated corpus for multilingual word sense disambiguation is out of reach for most languages even if resources are available. In this paper we propose an unsupervised method using word and sense embedding or improving the performance of these systems using untagged. Corpora and create two bags namely ontological bag and wiki sense bag to generate the senses with highest similarity. Wiki sense bag provides external knowledge to the system required to boost the disambiguation accuracy. We explore Word2Vec model to generate the sense bag and observe significant performance gain for our dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Wiki sense bag creation using multilingual word sense disambiguation

Patankar

Phadke

Devane

2022

IJ-AI

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Uma abordagem híbrida relacional para a desambiguação lexical de sentido na tradução automática

Specia¹

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Performance Analysis of Sense Embeddings in Multilingual WSD Framework

Itankar

Raza

2021

IJSRSET

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Execution of Word Sense Disambiguation (WSD) is one of the difficult undertakings in the space of Natural language processing (NLP). Age of sense clarified corpus for multilingual WSD is far off for most languages regardless of whether assets are accessible. In this paper we propose a solo technique utilizing word and sense embeddings for working on the presentation of WSD frameworks utilizing untagged corpora and make two bags to be specific context bag and wiki sense bag to create the faculties with most noteworthy closeness. Wiki sense bag gives outer information to the framework needed to help the disambiguation exactness. We investigate Word2Vec model to produce the sense back and notice huge execution acquire for our dataset.

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The Role of WSD for Multilingual Natural Language Applications

Cited by 3 publications

References 8 publications

Wiki sense bag creation using multilingual word sense disambiguation

Wiki sense bag creation using multilingual word sense disambiguation

Uma abordagem híbrida relacional para a desambiguação lexical de sentido na tradução automática

Performance Analysis of Sense Embeddings in Multilingual WSD Framework

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