NDLI: Short text classification by detecting information path

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Short text classification by detecting information path

Content Provider	ACM Digital Library
Author	Ding, Xuetao Zhang, Xiaochen Shen, Dou Cao, Bin Zhang, Shitao Jin, Xiaoming
Abstract	Short text is becoming ubiquitous in many modern information systems. Due to the shortness and sparseness of short texts, there are less informative word co-occurrences among them, which naturally pose great difficulty for classification tasks on such data. To overcome this difficulty, this paper proposes a new way for effectively classifying the short texts. Our method is based on a key observation that there usually exists ordered subsets in short texts, which is termed ``information path'' in this work, and classification on each subset based on the classification results of some pervious subsets can yield higher overall accuracy than classifying the entire data set directly. We propose a method to detect the information path and employ it in short text classification. Different from the state-of-art methods, our method does not require any external knowledge or corpus that usually need careful fine-tuning, which makes our method easier and more robust on different data sets. Experiments on two real world data sets show the effectiveness of the proposed method and its superiority over the existing methods.
Starting Page	727
Ending Page	732
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505638
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information path Short text classification Anchor shackle term
Content Type	Text
Resource Type	Article

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