Difference between revisions of "Wikipedia-Based Kernels for Dialogue Topic Tracking"
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| + | | title = Wikipedia-Based Kernels for Dialogue Topic Tracking | ||
| + | | date = 2014 | ||
| + | | authors = [[Seokhwan Kim]]<br />[[Rafael E. Banchs]]<br />[[Haizhou Li]] | ||
| + | | doi = 10.1109/ICASSP.2014.6853572 | ||
| + | | link = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6853572 | ||
| + | }} | ||
'''Wikipedia-Based Kernels for Dialogue Topic Tracking''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Seokhwan Kim]], [[Rafael E. Banchs]] and [[Haizhou Li]]. | '''Wikipedia-Based Kernels for Dialogue Topic Tracking''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Seokhwan Kim]], [[Rafael E. Banchs]] and [[Haizhou Li]]. | ||
== Overview == | == Overview == | ||
Dialogue topic tracking aims to segment on-going dialogues into topically coherent sub-dialogues and predict the topic category for each next segment. This paper proposes a kernel method for dialogue topic tracking to utilize various types of information obtained from [[Wikipedia]]. The experimental results show that proposed approach can significantly improve the performances of the task in mixed-initiative human-human dialogues. | Dialogue topic tracking aims to segment on-going dialogues into topically coherent sub-dialogues and predict the topic category for each next segment. This paper proposes a kernel method for dialogue topic tracking to utilize various types of information obtained from [[Wikipedia]]. The experimental results show that proposed approach can significantly improve the performances of the task in mixed-initiative human-human dialogues. | ||
Revision as of 07:34, 6 August 2019
Authors | Seokhwan Kim Rafael E. Banchs Haizhou Li |
|---|---|
Publication date | 2014 |
DOI | 10.1109/ICASSP.2014.6853572 |
Links | Original |
Wikipedia-Based Kernels for Dialogue Topic Tracking - scientific work related to Wikipedia quality published in 2014, written by Seokhwan Kim, Rafael E. Banchs and Haizhou Li.
Overview
Dialogue topic tracking aims to segment on-going dialogues into topically coherent sub-dialogues and predict the topic category for each next segment. This paper proposes a kernel method for dialogue topic tracking to utilize various types of information obtained from Wikipedia. The experimental results show that proposed approach can significantly improve the performances of the task in mixed-initiative human-human dialogues.
