@inproceedings{ae8b13b8949c4dc197d4f7c3372b28b9,
title = "Semantic parsing on freebase from question-answer pairs",
abstract = "In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main challenge in this setting is narrowing down the huge number of possible logical predicates for a given question. We tackle this problem in two ways: First, we build a coarse mapping from phrases to predicates using a knowledge base and a large text corpus. Second, we use a bridging operation to generate additional predicates based on neighboring predicates. On the dataset of Cai and Yates (2013), despite not having annotated logical forms, our system outperforms their state-of-the-art parser. Additionally, we collected a more realistic and challenging dataset of question-answer pairs and improves over a natural baseline.",
author = "Jonathan Berant and Andrew Chou and Roy Frostig and Percy Liang",
note = "Publisher Copyright: {\textcopyright} 2013 Association for Computational Linguistics.; 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013 ; Conference date: 18-10-2013 Through 21-10-2013",
year = "2013",
language = "الإنجليزيّة",
series = "EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1533--1544",
booktitle = "EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
address = "الولايات المتّحدة",
}