Abstract
In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.- Anthology ID:
- Q14-1030
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 2
- Month:
- Year:
- 2014
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 377–392
- Language:
- URL:
- https://aclanthology.org/Q14-1030
- DOI:
- 10.1162/tacl_a_00190
- Cite (ACL):
- Siva Reddy, Mirella Lapata, and Mark Steedman. 2014. Large-scale Semantic Parsing without Question-Answer Pairs. Transactions of the Association for Computational Linguistics, 2:377–392.
- Cite (Informal):
- Large-scale Semantic Parsing without Question-Answer Pairs (Reddy et al., TACL 2014)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/Q14-1030.pdf