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Query enrichment for web-query classification
| Content Provider | Microsoft Research |
|---|---|
| Author | Shen, Dou Pan, Rong Sun, Jian-Tao Pan, Jeffrey Junfeng Wu, Kangheng Yin, Jie Yang, Qiang |
| Copyright Year | 2006 |
| Abstract | Web search queries are typically short and ambiguous. To classify these queries into certain target categories is a dicult but important problem. In this paper, we present a new technique called query enrichment, which takes a short query and maps it to the intermediate objects. Based on the collected intermediate objects, the query is then mapped to the target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to ACM Knowledge-discovery and data mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall together, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this paper, we show that, despite the difficulty in an abundance of ambiguous queries and a lack of training data, our query enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result of F1 and precision are 42.4% and 44.4%, respectively, which are 9.6% and 24.3% higher than those from the runner-ups, respectively. |
| Starting Page | 320 |
| Ending Page | 352 |
| Page Count | 33 |
| ISSN | 10468188 15582868 |
| DOI | 10.1145/1165774.1165776 |
| Issue Number | 3 |
| Volume Number | 24 |
| Language | English |
| Publisher | ACM Permission to make digital/hard copy of all or part of this material without fee for personalor classroom use provided that the copies are not made or distributed for profit or commercial advantage, the ACM copyright/server notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc |
| Publisher Date | 2006-01-01 |
| Publisher Place | New York |
| Access Restriction | Open |
| Rights Holder | Microsoft Corporation |
| Subject Keyword | Machine learning intelligence |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems Business, Management and Accounting Computer Science Applications |
