Title
Normalizing Complex Functional Expressions in Japanese Predicates: Linguistically-Directed Rule-Based Paraphrasing and Its Application
Abstract
The growing need for text mining systems, such as opinion mining, requires a deep semantic understanding of the target language. In order to accomplish this, extracting the semantic information of functional expressions plays a crucial role, because functional expressions such as would like to and can’t are key expressions to detecting customers’ needs and wants. However, in Japanese, functional expressions appear in the form of suffixes, and two different types of functional expressions are merged into one predicate: one influences the factual meaning of the predicate while the other is merely used for discourse purposes. This triggers an increase in surface forms, which hinders information extraction systems. In this article, we present a novel normalization technique that paraphrases complex functional expressions into simplified forms that retain only the crucial meaning of the predicate. We construct paraphrasing rules based on linguistic theories in syntax and semantics. The results of experiments indicate that our system achieves a high accuracy of 79.7%, while it reduces the differences in functional expressions by up to 66.7%. The results also show an improvement in the performance of predicate extraction, providing encouraging evidence of the usability of paraphrasing as a means of normalizing different language expressions.
Year
DOI
Venue
2013
10.1145/2499955.2499959
ACM Trans. Asian Lang. Inf. Process.
Keywords
Field
DocType
functional expression,crucial role,factual meaning,different language expression,deep semantic understanding,complex functional expression,crucial meaning,linguistically-directed rule-based paraphrasing,different type,japanese predicates,information extraction system,complex functional expressions,predicate extraction,opinion mining,sentiment analysis,text mining
Rule-based system,Normalization (statistics),Expression (mathematics),Sentiment analysis,Computer science,Information extraction,Artificial intelligence,Natural language processing,Predicate (grammar),Syntax,Semantics
Journal
Volume
Issue
Citations 
12
3
0
PageRank 
References 
Authors
0.34
8
6
Name
Order
Citations
PageRank
Tomoko Izumi114121.33
Kenji Imamura231.12
Taichi Asami32210.49
Kuniko Saito4757.12
Gen-ichiro Kikui530533.43
Satoshi Sato630.78