Sentence and Word Embedding Employed in Open Question-Answering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100739" target="_blank" >RIV/00216224:14330/18:00100739 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sentence and Word Embedding Employed in Open Question-Answering

Popis výsledku v původním jazyce

The Automatic Question Answering, or AQA, system is a representative of open domain QA systems, where the answer selection process leans on syntactic and semantic similarities between the question and the answering text snippets. Such approach is specifically oriented to languages with fine grained syntactic and morphologic features that help to guide the correct QA match. In this paper, we present the latest results of the AQA system with new word embedding criteria implementation. All AQA processing steps (question processing, answer selection and answer extraction) are syntax-based with advanced scoring obtained by a combination of several similarity criteria (TF-IDF, tree distance, ...). Adding the word embedding parameters helped to resolve the QA match in cases, where the answer is expressed by semantically near equivalents. We describe the design and implementation of the whole QA process and provide a new evaluation of the AQA system with the word embedding criteria measured with an expanded version of Simple Question-Answering Database, or SQAD, with more than 3000 question-answer pairs extracted from the Czech Wikipedia.

Název v anglickém jazyce

Sentence and Word Embedding Employed in Open Question-Answering

Popis výsledku anglicky

The Automatic Question Answering, or AQA, system is a representative of open domain QA systems, where the answer selection process leans on syntactic and semantic similarities between the question and the answering text snippets. Such approach is specifically oriented to languages with fine grained syntactic and morphologic features that help to guide the correct QA match. In this paper, we present the latest results of the AQA system with new word embedding criteria implementation. All AQA processing steps (question processing, answer selection and answer extraction) are syntax-based with advanced scoring obtained by a combination of several similarity criteria (TF-IDF, tree distance, ...). Adding the word embedding parameters helped to resolve the QA match in cases, where the answer is expressed by semantically near equivalents. We describe the design and implementation of the whole QA process and provide a new evaluation of the AQA system with the word embedding criteria measured with an expanded version of Simple Question-Answering Database, or SQAD, with more than 3000 question-answer pairs extracted from the Czech Wikipedia.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

60200 - Languages and Literature

Projekt

<a href="/cs/project/GA15-13277S" target="_blank" >GA15-13277S: Hyperintensionální logika pro analýzu přirozeného jazyka</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018)

ISBN

9789897582752

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

486-492

Název nakladatele

SCITEPRESS - Science and Technology Publications

Místo vydání

Setúbal, Portugal

Místo konání akce

Funchal, Madeira, Portugal

Datum konání akce

16. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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