Short text understanding through lexical-semantic analysis

Hua, Wen, Wang, Zhongyuan, Wang, Haixun, Zheng, Kai and Zhou, Xiaofang (2015). Short text understanding through lexical-semantic analysis. In: 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015. IEEE International Conference on Data Engineering, Seoul, South Korea, (495-506). 13-17 April 2015. doi:10.1109/ICDE.2015.7113309


Author Hua, Wen
Wang, Zhongyuan
Wang, Haixun
Zheng, Kai
Zhou, Xiaofang
Title of paper Short text understanding through lexical-semantic analysis
Conference name IEEE International Conference on Data Engineering
Conference location Seoul, South Korea
Conference dates 13-17 April 2015
Convener IEEE
Proceedings title 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015   Check publisher's open access policy
Journal name Proceedings - International Conference on Data Engineering   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/ICDE.2015.7113309
Open Access Status Not Open Access
ISBN 9781479979639
9781479979646
ISSN 1084-4627
Volume 2015-May
Start page 495
End page 506
Total pages 12
Collection year 2016
Abstract/Summary Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing methods cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text processing such as topic modeling. Third, short texts are usually more ambiguous. We argue that knowledge is needed in order to better understand short texts. In this work, we use lexical-semantic knowledge provided by a well-known semantic network for short text understanding. Our knowledge-intensive approach disrupts traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are effective in harvesting semantics of short texts.
Subjects 1710 Information Systems
1711 Signal Processing
1712 Software
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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