Using first-order logic to compress sentences
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
item.page.type
Conference Paper
item.page.format
Text
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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