Projects per year
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.
|Number of pages||16|
|Journal||Transactions of the Association for Computational Linguistics|
|Publication status||Published - 1 Oct 2014|
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- 2 Finished
Xperience - 'Robotes Bootstrapped through Learning from Experience'
Steedman, M., Geib, C. & Petrick, R.
1/01/10 → 31/12/15
- School of Informatics - Professor
- Institute of Language, Cognition and Computation
- Language, Interaction and Robotics
Person: Academic: Research Active