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Content Provider | ACM Digital Library |
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Author | Wang, Yue Tang, Jian Mei, Qiaozhu Zheng, Kai |
Abstract | Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets. |
Starting Page | 1105 |
Ending Page | 1113 |
Page Count | 9 |
File Format | |
ISBN | 9781450348874 |
DOI | 10.1145/3097983.3098166 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2017-08-13 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Memory networks Short text classification Query expansion |
Content Type | Text |
Resource Type | Article |
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