Abstract
Transforming a natural language (NL) question into a corresponding logical form (LF) is central to the knowledge-based question answering (KB-QA) task. Unlike most previous methods that achieve this goal based on mappings between lexicalized phrases and logical predicates, this paper goes one step further and proposes a novel embedding-based approach that maps NL-questions into LFs for KBQA by leveraging semantic associations between lexical representations and KBproperties in the latent space. Experimental results demonstrate that our proposed method outperforms three KB-QA baseline methods on two publicly released QA data sets.
Original language | English |
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Title of host publication | EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 645-650 |
Number of pages | 6 |
ISBN (Electronic) | 9781937284961 |
DOIs | |
Publication status | Published - 2014 |
Event | 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 - Doha, Qatar Duration: 2014 Oct 25 → 2014 Oct 29 |
Publication series
Name | EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference |
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Other
Other | 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 |
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Country/Territory | Qatar |
City | Doha |
Period | 14/10/25 → 14/10/29 |
Bibliographical note
Publisher Copyright:© 2014 Association for Computational Linguistics.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Vision and Pattern Recognition
- Information Systems