A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Capturing term dependencies using a language model based on sentence trees
Proceedings of the eleventh international conference on Information and knowledge management
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Probabilistic information retrieval model for a dependency structured indexing system
Information Processing and Management: an International Journal
Text Mining Application Programming (Programming Series)
Text Mining Application Programming (Programming Series)
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Information Retrieval: Implementing and Evaluating Search Engines
Information Retrieval: Implementing and Evaluating Search Engines
Retrieving customary web language to assist writers
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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In this paper, an approach to automatic optimisation of the retrieval quality of search engines using a language model paradigm is presented. The topics of information retrieval (IR) and natural language processing (NLP) have already been investigated. However, most of the approaches were focused on learning retrieval functions from existing examples and pre-set feature lists. Others used surface statistics in the form of n-grams or efficient parse tree utilisations --- either performs poorly with a language open to changes. Intuitively, an IR system should present relevant documents high in its ranking, with less relevant following below. To accomplish that, semantics/ontologies, usage of grammatical information and document structure analysis were researched. An evolutionary enrichment of language model for typed dependency analysis acquired from documents and queries can adapt the system to the texts encountered. Futhermore, the results in controlled experiments verify the possibility of outperforming existing approaches in terms of retrieval quality.