Incorporating Task-Oriented Representation in Text Classification
Refereed conference paper presented and published in conference proceedings


Times Cited
Altmetrics Information
.

Other information
AbstractText classification (TC) is an important task in natural language processing. Recently neural network has been applied to text classification and achieves significant improvement in performance. Since some documents are short and ambiguous, recent research enriches document representation with concepts of words extracted from an external knowledge base. However, this approach might incorporate task-irrelevant concepts or coarse granularity concepts that could not discriminate classes in a TC task. This might add noise to document representation and degrade TC performance. To tackle this problem, we propose a task-oriented representation that captures word-class relevance as task-relevant information. We integrate task-oriented representation in a CNN classification model to perform TC. Experimental results on widely used datasets show our approach outperforms comparison models.
All Author(s) ListXue Lei, Yi Cai, Jingyun Xu, Da Ren, Qing Li, Ho-fung Leung
Name of Conference24th International Conference on Database Systems for Advanced Applications
Start Date of Conference22/04/2019
End Date of Conference25/04/2019
Place of ConferenceChiang Mai
Country/Region of ConferenceThailand
Proceedings TitleDATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2019), PT II
Year2019
PublisherSPRINGER
Pages401 - 415
ISSN0302-9743
LanguagesEnglish-United States

Last updated on 2020-04-08 at 01:03