Unsupervised Word Sense Disambiguation Using Word Embeddings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405583" target="_blank" >RIV/00216208:11320/19:10405583 - isvavai.cz</a>

Výsledek na webu

<a href="https://fruct.org/publications/fruct25/files/Mor.pdf" target="_blank" >https://fruct.org/publications/fruct25/files/Mor.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Word Sense Disambiguation Using Word Embeddings

Popis výsledku v původním jazyce

Word sense disambiguation is the task of assigning the correct sense of a polysemous word in the context in which it appears. In recent years, word embeddings have been applied successfully to many NLP tasks. Thanks to their ability to capture distributional semantics, more recent attention have been focused on utilizing word embeddings to disambiguate words. In this paper, a novel unsupervised method is proposed to disambiguate words from the first language by deploying a trained word embeddings model of the second language using only a bilingual dictionary. While the translated words are useful clues for the disambiguation process, the main idea of this work is to use the information provided by English-translated surrounding words to disambiguate Persian words using trained English word2vec; well-known word embeddings model. Each translation of the polysemous word is compared against word embeddings of translated surrounding words to calculate word similarity scores and the most similar word to vec

Název v anglickém jazyce

Unsupervised Word Sense Disambiguation Using Word Embeddings

Popis výsledku anglicky

Word sense disambiguation is the task of assigning the correct sense of a polysemous word in the context in which it appears. In recent years, word embeddings have been applied successfully to many NLP tasks. Thanks to their ability to capture distributional semantics, more recent attention have been focused on utilizing word embeddings to disambiguate words. In this paper, a novel unsupervised method is proposed to disambiguate words from the first language by deploying a trained word embeddings model of the second language using only a bilingual dictionary. While the translated words are useful clues for the disambiguation process, the main idea of this work is to use the information provided by English-translated surrounding words to disambiguate Persian words using trained English word2vec; well-known word embeddings model. Each translation of the polysemous word is compared against word embeddings of translated surrounding words to calculate word similarity scores and the most similar word to vec

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 25th Conference of Open Innovations Association FRUCT 2019

ISBN

978-952-69244-0-3

ISSN

2305-7254

e-ISSN

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Počet stran výsledku

6

Strana od-do

228-233

Název nakladatele

Finnish-Russian University Cooperation in Telecommunications

Místo vydání

Helsinki, Finland

Místo konání akce

Helsinki, Finland

Datum konání akce

5. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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