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| Content Provider | ACM Digital Library |
|---|---|
| Author | Sun, Aixin |
| Abstract | We propose a simple, scalable, and non-parametric approach for short text classification. Leveraging the well studied and scalable Information Retrieval (IR) framework, our approach mimics human labeling process for a piece of short text. It first selects the most representative and topical-indicative words from a given short text as query words, and then searches for a small set of labeled short texts best matching the query words. The predicted category label is the majority vote of the search results. Evaluated on a collection of more than 12K Web snippets, the proposed approach achieves comparable classification accuracy with the baseline Maximum Entropy classifier using as few as 3 query words and top-5 best matching search hits. Among the four query word selection schemes proposed and evaluated in our experiments, term frequency together with clarity gives the best classification accuracy. |
| Starting Page | 1145 |
| Ending Page | 1146 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450314725 |
| DOI | 10.1145/2348283.2348511 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-08-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Search and vote Short text classification |
| Content Type | Text |
| Resource Type | Article |
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