LREC 2006 - Proceedings sorted by papers

Title	Creation of a Japanese Adverb Dictionary that Includes Information on the Speaker’s Communicative Intention Using Machine Learning
Authors	T. Kanamaru, M. Murata, H. Isahara
Abstract	Japanese adverbs are classified as either declarative or normal; the former declare the communicative intention of the speaker, while the latter convey a manner of action, a quantity, or a degree by which the adverb modifies the verb or adjective that it accompanies. We have automatically classified adverbs as either declarative or not declarative using a machine-learning method such as the maximum entropy method. We defined adverbs having positive or negative connotations as the positive data. We classified adverbs in the EDR dictionary and IPADIC used by Chasen using this result and built an adverb dictionary that contains descriptions of the communicative intentions of the speaker.
Keywords
Full paper	Creation of a Japanese Adverb Dictionary that Includes Information on the Speaker’s Communicative Intention Using Machine Learning

Title

Creation of a Japanese Adverb Dictionary that Includes Information on the Speaker’s Communicative Intention Using Machine Learning

Authors

T. Kanamaru, M. Murata, H. Isahara

Abstract

Japanese adverbs are classified as either declarative or normal; the former declare the communicative intention of the speaker, while the latter convey a manner of action, a quantity, or a degree by which the adverb modifies the verb or adjective that it accompanies. We have automatically classified adverbs as either declarative or not declarative using a machine-learning method such as the maximum entropy method. We defined adverbs having positive or negative connotations as the positive data. We classified adverbs in the EDR dictionary and IPADIC used by Chasen using this result and built an adverb dictionary that contains descriptions of the communicative intentions of the speaker.

Keywords

Full paper

Creation of a Japanese Adverb Dictionary that Includes Information on the Speaker’s Communicative Intention Using Machine Learning