A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval
Proceedings of the eighth international conference on Information and knowledge management
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Query structuring with two-stage term dependence in the japanese language
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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This paper proposes a query structuring method by analyzing Japanese natural language sentences that users input. We use linguistic structure information obtained by morphological analysis and dependency parsing on the natural language inputs. Our proposed method converts a sentence into a structured query on the basis of linguistic structures such as phrases and modification relations. Such structured queries enable effective retrieval with reasonable efficiency. To evaluate our proposed method, we compare it with the method using no structures at all, using a 100-gibabyte web collection mostly written in Japanese. We demonstrate through the experiments that mean average precision was improved about 8.6% using our query structuring method alone, and about 22.5% by combining our query structuring method with pseudo-relevance feedback.