An analysis of Speech Acts for Korean Using Support Vector Machines


The KIPS Transactions:PartB , Vol. 12, No. 3, pp. 365-368, Jun. 2005
10.3745/KIPSTB.2005.12.3.365,   PDF Download:

Abstract

We propose a speech.act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech.act of each utterance. In experiment, we acquired overall 90.54% of accuracy with dialogue corpus for hotel reservation domain.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
J. M. En, S. W. Lee, J. Y. Seo, "An analysis of Speech Acts for Korean Using Support Vector Machines," The KIPS Transactions:PartB , vol. 12, no. 3, pp. 365-368, 2005. DOI: 10.3745/KIPSTB.2005.12.3.365.

[ACM Style]
Jong Min En, Song Wook Lee, and Jung Yun Seo. 2005. An analysis of Speech Acts for Korean Using Support Vector Machines. The KIPS Transactions:PartB , 12, 3, (2005), 365-368. DOI: 10.3745/KIPSTB.2005.12.3.365.