Solvers for Mathematical Word Problems in Czech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345998" target="_blank" >RIV/68407700:21230/20:00345998 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2718/paper09.pdf" target="_blank" >http://ceur-ws.org/Vol-2718/paper09.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Solvers for Mathematical Word Problems in Czech

Popis výsledku v původním jazyce

We study the task of an automatic evaluation of mathematical word problems, which belongs to the category of natural language processing and has become popular in recent years. Since all the so far published methods were developed for inputs in English, our goal is to review them and propose solutions able to cope with inputs in the Czech language. We face the question whether we can achieve a competitive accuracy for a natural language with flexible word order, and with the assumption that only a relatively small dataset of training and testing data is available. We propose and evaluate two methods. One relies on a rule-based processing of dependency trees computed by UDPipe. The other method builds on machine learning. It transforms word problems into numeric vectors and trains SVM to classify them. We also show that it improves in a combination with a search for predefined sequences of words and word classes, achieving 75% accuracy on our dataset of 500 Czech word problems.

Název v anglickém jazyce

Solvers for Mathematical Word Problems in Czech

Popis výsledku anglicky

We study the task of an automatic evaluation of mathematical word problems, which belongs to the category of natural language processing and has become popular in recent years. Since all the so far published methods were developed for inputs in English, our goal is to review them and propose solutions able to cope with inputs in the Czech language. We face the question whether we can achieve a competitive accuracy for a natural language with flexible word order, and with the assumption that only a relatively small dataset of training and testing data is available. We propose and evaluate two methods. One relies on a rule-based processing of dependency trees computed by UDPipe. The other method builds on machine learning. It transforms word problems into numeric vectors and trains SVM to classify them. We also show that it improves in a combination with a search for predefined sequences of words and word classes, achieving 75% accuracy on our dataset of 500 Czech word problems.

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/GA19-21198S" target="_blank" >GA19-21198S: Složité predikční modely a jejich učení z částečně anotovaných dat</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 20th Conference Information Technologies - Applications and Theory (ITAT 2020)

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

8

Strana od-do

18-25

Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Aachen

Místo konání akce

hotel Tyrapol, Oravská Lesná

Datum konání akce

18. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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