Understanding Search Queries in Natural Language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00109726" target="_blank" >RIV/00216224:14330/18:00109726 - isvavai.cz</a>

Výsledek na webu

<a href="https://nlp.fi.muni.cz/raslan/2018/paper07-Neverilova_Kvassay.pdf" target="_blank" >https://nlp.fi.muni.cz/raslan/2018/paper07-Neverilova_Kvassay.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Understanding Search Queries in Natural Language

Popis výsledku v původním jazyce

This work is part of a project aiming to provide one single search endpoint for all company data. We present a search query parser that takes a speech-to-text output, i.e. a sentence. The output is a structured representation of the search query from which a SPARQL query is generated. The SPARQL is then applied to an ontology with the company data. The parsing procedure consists of two steps. First, the search intent is detected, second, the query is parsed based on the search intent. For the intent classification, we use word embeddings with boosting of top 5 words, and support vector machines. For the parsing, we use semantic role labeling, named entity recognition, and external resources such as ConceptNet and DBPedia. The final parsing step is rule-based and related to the ontology structure. The intent classifier accuracy is 94%. In the subsequent manual evaluation,the resulting structures were complete and correct in 51% cases, in 34.57% of cases it was complete and correct but it also contained irrelevant information.

Název v anglickém jazyce

Understanding Search Queries in Natural Language

Popis výsledku anglicky

This work is part of a project aiming to provide one single search endpoint for all company data. We present a search query parser that takes a speech-to-text output, i.e. a sentence. The output is a structured representation of the search query from which a SPARQL query is generated. The SPARQL is then applied to an ontology with the company data. The parsing procedure consists of two steps. First, the search intent is detected, second, the query is parsed based on the search intent. For the intent classification, we use word embeddings with boosting of top 5 words, and support vector machines. For the parsing, we use semantic role labeling, named entity recognition, and external resources such as ConceptNet and DBPedia. The final parsing step is rule-based and related to the ontology structure. The intent classifier accuracy is 94%. In the subsequent manual evaluation,the resulting structures were complete and correct in 51% cases, in 34.57% of cases it was complete and correct but it also contained irrelevant information.

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

<a href="/cs/project/EF16_013%2F0001781" target="_blank" >EF16_013/0001781: LINDAT/CLARIN - Výzkumná infrastruktura pro jazykové technologie - rozšíření repozitáře a výpočetní kapacity</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 Recent Advances in Slavonic Natural Language Processing, RASLAN 2018

ISBN

9788026315179

ISSN

2336-4289

e-ISSN

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Počet stran výsledku

9

Strana od-do

85-93

Název nakladatele

Tribun EU

Místo vydání

Brno

Místo konání akce

Karlova Studánka

Datum konání akce

7. 12. 2018

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000612420300011