A Bootstrapping Approach to Unsupervised Detection of Cue Phrase Variants

We investigate the unsupervised detection of semi-fixed cue phrases such as "This paper proposes a novel approach. . . 1" from unseen text, on the basis of only a handful of seed cue phrases with the desired semantics. The problem, in contrast to bootstrapping approaches for Question Answering and Information Extraction, is that it is hard to find a constraining context for occurrences of semi-fixed cue phrases. Our method uses components of the cue phrase itself, rather than external context, to bootstrap. It successfully excludes phrases which are different from the target semantics, but which look superficially similar. The method achieves 88% accuracy, outperforming standard bootstrapping approaches.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Rashid M. Abdalla, Simone Teufel
• Keywords: ACL 2006, ACL 2007, Cue Phrase, Seed Cue Phrases, Semi-fixed Cue Phrases