Topic detection based on dialogue history

Authors:
Takayuki Nakata;Shinichi Ando;Akitoshi Okumura
Affiliations:
NEC Corporation, Kanagawa, Japan;NEC Corporation, Kanagawa, Japan;NEC Corporation, Kanagawa, Japan
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

Citing 4
Cited 0

Automatic text processing: the transformation, analysis, and retrieval of information by computer

Automatic text processing: the transformation, analysis, and retrieval of information by computer
Expert network: effective and efficient learning from human decisions in text categorization and retrieval

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Approaches for Detecting and Tracking News Events

IEEE Intelligent Systems
Automatic interpretation system integrating free-style sentence translation and parallel text based translation

S2S '02 Proceedings of the ACL-02 workshop on Speech-to-speech translation: algorithms and systems - Volume 7

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a topic detection method using a dialogue history for a speech translator. The method uses a k-nearest neighbor method for the algorithm, automatically clusters target topics into smaller topics grouped by similarity, and incorporates dialogue history weighted in terms of time to detect and track topics on spoken phrases. From the evaluation of detection performance using test data comprised of realistic spoken dialogue, the method has shown to perform better with clustering incorporated, and when combined with dialogue history of three sentences, gives detection accuracy of 72.1%.