Exploring web scale language models for search query processing
| Autor: | Kuansan Wang, Jiangbo Miao, Jian Huang, Fritz Behr, Xiaolong Li, Jianfeng Gao, C. Lee Giles |
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| Rok vydání: | 2010 |
| Předmět: |
Language identification
Computer science computer.software_genre Query language Query optimization Ranking (information retrieval) Query expansion Search engine Web query classification Data control language Query by Example computer.programming_language Anchor text Information retrieval Concept search Web search query business.industry Information extraction Object Query Language Sargable Language model Artificial intelligence business computer Natural language processing RDF query language |
| Zdroj: | WWW |
| DOI: | 10.1145/1772690.1772737 |
| Popis: | It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document. Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval, yet a large scale analysis on the extent of the language differences has been lacking. In this paper, we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document: the body, the title, and the anchor text. Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles, offering a quantitative explanation to the observations in past work.We apply these web scale n-gram language models to three search query processing (SQP) tasks: query spelling correction, query bracketing and long query segmentation. By controlling the size and the order of different language models, we find that the perplexity metric to be a good accuracy indicator for these query processing tasks. We show that using smoothed language models yields significant accuracy gains for query bracketing for instance, compared to using web counts as in the literature. We also demonstrate that applying web-scale language models can have marked accuracy advantage over smaller ones. |
| Databáze: | OpenAIRE |
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