Multi-Style Language Model for Web Scale Information Retrieval

Multi-Style Language Model for Web Scale Information Retrieval

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by Kuansan Wang , Xiaolong Li , Jianfeng Gao

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Datum	Value	Source
TITLE	Multi-Style Language Model for Web Scale Information Retrieval	SVM HeaderParse 0.2
AUTHOR NAME	Kuansan Wang	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft Way	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052 USA	SVM HeaderParse 0.2
AUTHOR NAME	Xiaolong Li	SVM HeaderParse 0.2
AUTHOR AFFIL	Bing Search Ranking & Intent Group; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052 USA	SVM HeaderParse 0.2
AUTHOR NAME	Jianfeng Gao	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052 USA	SVM HeaderParse 0.2
ABSTRACT	Web documents are typically associated with many text streams, including the body, the title and the URL that are determined by the authors, and the anchor text or search queries used by others to refer to the documents. Through a systematic large scale analysis on their cross entropy, we show that these text streams appear to be composed in different language styles, and hence warrant respective language models to properly describe their properties. We propose a language modeling approach to Web document retrieval in which each document is characterized by a mixture model with components corresponding to the various text streams associated with the document. Immediate issues for such a mixture model arise as all the text streams are not always present for the documents, and they do not share the same lexicon, making it challenging to properly combine the statistics from the mixture components. To address these issues, we introduce an “openvocabulary” smoothing technique so that all the component language models have the same cardinality and their scores can simply be linearly combined. To ensure that the approach can cope with Web scale applications, the model training algorithm is designed to require no labeled data and can be fully automated with few heuristics and no empirical parameter tunings. The evaluation on Web document ranking tasks shows that the component language models indeed have varying degrees of capabilities as predicted by the cross-entropy analysis, and the combined mixture model outperforms the state-of-the-art BM25F based system.	SVM HeaderParse 0.2
CITATIONS	32 found	ParsCit 1.0