2017
ACL
ACL 2017
Semantic Parsing of Pre-university Math Problems
Abstract
AbstractWe 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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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Knowledge & Reasoning and Natural Language Processing
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Keyword Pioneer
— math problem
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Hot Topic Early Bird
— mathematical reasoning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Parsing
Natural Language Processing > Understanding > Semantic Analysis
Knowledge & Reasoning > Reasoning > Automated Reasoning
Interdisciplinary > Linguistics > Semantics
Natural Language Processing > Applications > Semantic Parsing
Artificial Intelligence > Core AI > Natural Language Processing