Affiliations: [a] School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu, China | [b] Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Anhui, China | [c] School of Computer Science and Information Engineering, Hefei University of Technology, Anhui, China | [d] School of Electronics an Information Engineering, Anhui Jianzhu University, Anhui, China
Correspondence:
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Corresponding author: Yi Zhu, School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu, China. E-mail: [email protected].
Abstract: Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.
Keywords: Extreme short text classification, keywords screening, attention mechanisms
