A Generic Sentence Trimmer with CRFs

The paper presents a novel sentence trimmer in Japanese, which combines a non-statistical yet generic tree generation model and Conditional Random Fields (CRFs), to address improving the grammaticality of compression while retaining its relevance. Experiments found that the present approach outperforms in grammaticality and in relevance a dependency-centric approach (Oguro et al., 2000; Morooka et al., 2004; Yamagata et al., 2006; Fukutomi et al., 2007) - the only line of work in prior literature (on Japanese compression) we are aware of that allows replication and permits a direct comparison.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Tadashi Nomoto
• Keywords: ACL 2008, Computational Linguistics, Conditional Random Fields, Generic Tree Generation, Present Approach Outperforms