Semantic Parsing of Pre-university Math Problems

Takuya Matsuzaki, Takumi Ito, Hidenao Iwane, Hirokazu Anai, Noriko H. Arai


Abstract
We have been developing an end-to-end math problem solving system that accepts natural language input. The current paper focuses on how we analyze the problem sentences to produce logical forms. We chose a hybrid approach combining a shallow syntactic analyzer and a manually-developed lexicalized grammar. A feature of the grammar is that it is extensively typed on the basis of a formal ontology for pre-university math. These types are helpful in semantic disambiguation inside and across sentences. Experimental results show that the hybrid system produces a well-formed logical form with 88% precision and 56% recall.
Anthology ID:
P17-1195
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2131–2141
Language:
URL:
https://www.aclweb.org/anthology/P17-1195
DOI:
10.18653/v1/P17-1195
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1195.pdf