@inproceedings{0cc45565e1f44c12a4e3583a67e1583a,
title = "Joint relational embeddings for knowledge-based question answering",
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.",
author = "Yang, {Min Chul} and Nan Duan and Ming Zhou and Rim, {Hae Chang}",
note = "Publisher Copyright: {\textcopyright} 2014 Association for Computational Linguistics.; 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 ; Conference date: 25-10-2014 Through 29-10-2014",
year = "2014",
doi = "10.3115/v1/d14-1071",
language = "English",
series = "EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "645--650",
booktitle = "EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
}