Using first-order logic to compress sentences

Files

IDL-51241.pdf (369.59 KB)

Date

2012

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Abstract

Sentence compression is one of the most challenging tasks in natural language processing, which may be of increasing interest to many applications such as abstractive summarization and text simplification for mobile devices. In this paper, we present a novel sentence compression model based on first-order logic, using Markov Logic Network. Sentence compression is formulated as a word/phrase deletion problem in this model. By taking advantage of first-order logic, the proposed method is able to incorporate local linguistic features and to capture global dependencies between word deletion operations. Experiments on both written and spoken corpora show that our approach produces competitive performance against the state-of-the-art methods in terms of manual evaluation measures such as importance, grammaticality, and overall quality.

Description

Meeting: Twenty-Sixth AAAI Conference on Artificial Intelligence

Keywords

LINGUISTICS, MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, NATURAL LANGUAGE PROCESSING

Citation

Minlie Huang, Xing Shi, Feng Jin, & Xiaoyan Zhu (2012).Using First-Order Logic to Compress Sentences. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 1657-1663.

URI

http://hdl.handle.net/10625/51241
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