Latent semantics in language models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43924529" target="_blank" >RIV/49777513:23520/15:43924529 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.csl.2015.01.004" target="_blank" >http://dx.doi.org/10.1016/j.csl.2015.01.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csl.2015.01.004" target="_blank" >10.1016/j.csl.2015.01.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Latent semantics in language models

Popis výsledku v původním jazyce

This paper investigates three different sources of information and their integration into language modelling. Global semantics is modelled by Latent Dirichlet allocation and brings long range dependencies into language models. Word clusters given by semantic spaces enrich these language models with short range semantics. Finally, our own stemming algorithm is used to further enhance the performance of language modelling for inflectional languages. Our research shows that these three sources of information enrich each other and their combination dramatically improves language modelling. All investigated models are acquired in a fully unsupervised manner. We show the efficiency of our methods for several languages such as Czech, Slovenian, Slovak, Polish, Hungarian, and English, proving their multilingualism. The perplexity tests are accompanied by machine translation tests that prove the ability of the proposed models to improve the performance of a real-world application.

Název v anglickém jazyce

Latent semantics in language models

Popis výsledku anglicky

This paper investigates three different sources of information and their integration into language modelling. Global semantics is modelled by Latent Dirichlet allocation and brings long range dependencies into language models. Word clusters given by semantic spaces enrich these language models with short range semantics. Finally, our own stemming algorithm is used to further enhance the performance of language modelling for inflectional languages. Our research shows that these three sources of information enrich each other and their combination dramatically improves language modelling. All investigated models are acquired in a fully unsupervised manner. We show the efficiency of our methods for several languages such as Czech, Slovenian, Slovak, Polish, Hungarian, and English, proving their multilingualism. The perplexity tests are accompanied by machine translation tests that prove the ability of the proposed models to improve the performance of a real-world application.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0090" target="_blank" >ED1.1.00/02.0090: NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Computer Speech and language

ISSN

0885-2308

e-ISSN

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Svazek periodika

33

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

88-108

Kód UT WoS článku

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EID výsledku v databázi Scopus

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