Flexible Japanese Sentence Compression by Relaxing Unit Constraints
Jun Harashima, Sadao Kurohashi · International Conference on Computational Linguistics · 2012
Sentence compression is important in a wide range of applications in natural language processing. Previous approaches of Japanese sentence compression can be divided into two groups. Word-based methods extract a subset of words from a sentence to shorten it, while bunsetsubased methods extract a subset of bunsetsu (where a bunsetsu is a text unit that consists of content words and following function words). Basically, bunsetsu-based methods perform better than word-based methods. However, bunsetsu-based methods have the disadvantage that they cannot drop unimportant words from each bunsetsu because they have to follow constraints under which each bunsetsu is treated as a unit. In this paper, we propose a novel compression method to overcome this disadvantage. Our method relaxes the constraints using Lagrangian relaxation and shortens each bunsetsu if it contains unimportant words. Experimental results show that our method effectively compresses a sentence while preserving its important information and grammaticality.