@inproceedings{b78263f21579445184989f1aa5436ea1,
title = "Incorporating task-oriented representation in text classification",
abstract = "Text 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.",
keywords = "Natural language processing, Neural network, Text classification",
author = "Xue Lei and Yi Cai and Jingyun Xu and Da Ren and Qing Li and Leung, {Ho fung}",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-18579-4_24",
language = "English",
isbn = "9783030185787",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "401--415",
editor = "Guoliang Li and Jun Yang and Joao Gama and Juggapong Natwichai and Yongxin Tong",
booktitle = "Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings",
note = "24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 ; Conference date: 22-04-2019 Through 25-04-2019",
}