@inproceedings{matsuzaki-etal-2017-semantic,
title = "Semantic Parsing of Pre-university Math Problems",
author = "Matsuzaki, Takuya and
Ito, Takumi and
Iwane, Hidenao and
Anai, Hirokazu and
Arai, Noriko H.",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1195",
doi = "10.18653/v1/P17-1195",
pages = "2131--2141",
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.",
}
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%0 Conference Proceedings
%T Semantic Parsing of Pre-university Math Problems
%A Matsuzaki, Takuya
%A Ito, Takumi
%A Iwane, Hidenao
%A Anai, Hirokazu
%A Arai, Noriko H.
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F matsuzaki-etal-2017-semantic
%X 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.
%R 10.18653/v1/P17-1195
%U https://aclanthology.org/P17-1195
%U https://doi.org/10.18653/v1/P17-1195
%P 2131-2141
Markdown (Informal)
[Semantic Parsing of Pre-university Math Problems](https://aclanthology.org/P17-1195) (Matsuzaki et al., ACL 2017)
ACL
- Takuya Matsuzaki, Takumi Ito, Hidenao Iwane, Hirokazu Anai, and Noriko H. Arai. 2017. Semantic Parsing of Pre-university Math Problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2131–2141, Vancouver, Canada. Association for Computational Linguistics.