Abbreviation generation for Japanese multi-word expressions
Hiromi Wakaki, Hiroko Fujii, Masaru Suzuki, Mika Fukui, Kazuo Sumita · 2009
This paper proposes a novel method for generating Japanese abbreviations from their full forms with the Log-Linear Model (LLM) in order to take advantage of characteristic patterns of Japanese abbreviation. Our experimental results show that the method is effective for TV program titles that contain colloquial expressions. The proposed method achieved 78.8% recall for the top 30 candidates, whereas a baseline method using Conditional Random Fields (CRFs) achieved 68.3% recall. Moreover, from the results of experiments using six data sets classified according to types of character and semantic categories, we show that each performance of the above two methods depends on the types of the full forms.