Semantic Parsing of Pre-university Math Problems
Takuya Matsuzaki, Takumi Ito, Hidenao Iwane, Hirokazu Anai, Noriko Arai · 2017
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 manuallydeveloped 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.