Advanced feedback methods in information retrieval
Journal of the American Society for Information Science
An approach to natural language for document retrieval
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Outline of a knowledge base model for an intelligent information retrieval system
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Experiments on incorporating syntactic processing of user queries into a document retrieval strategy
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Natural language techniques for intelligent information retrieval
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Class-based n-gram models of natural language
Computational Linguistics
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating syntactic information into a document retrieval strategy: an investigation
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
An information retrieval system based on artificial intelligence techniques
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Precision Weighting—An Effective Automatic Indexing Method
Journal of the ACM (JACM)
Generic summaries for indexing in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
On Collection Size and Retrieval Effectiveness
Information Retrieval
A study of parameter tuning for term frequency normalization
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Joining statistics with NLP for text categorization
ANLC '92 Proceedings of the third conference on Applied natural language processing
Syntactic approaches to automatic book indexing
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
TTP: a fast and robust parser for natural language
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Lexical query paraphrasing for document retrieval
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Examining the content load of part of speech blocks for information retrieval
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
Terrier information retrieval platform
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Natural language technology and query expansion: issues, state-of-the-art and perspectives
Journal of Intelligent Information Systems
Localised topic information extraction for summarisation using syntactic sequences
International Journal of Knowledge and Web Intelligence
Compact query term selection using topically related text
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Whereas in language words of high frequency are generally associated with low content [Bookstein, A., & Swanson, D. (1974). Probabilistic models for automatic indexing. Journal of the American Society of Information Science, 25(5), 312-318; Damerau, F. J. (1965). An experiment in automatic indexing. American Documentation, 16, 283-289; Harter, S. P. (1974). A probabilistic approach to automatic keyword indexing. PhD thesis, University of Chicago; Sparck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11-21; Yu, C., & Salton, G. (1976). Precision weighting - an effective automatic indexing method. Journal of the Association for Computer Machinery (ACM), 23(1), 76-88], shallow syntactic fragments of high frequency generally correspond to lexical fragments of high content [Lioma, C., & Ounis, I. (2006). Examining the content load of part of speech blocks for information retrieval. In Proceedings of the international committee on computational linguistics and the association for computational linguistics (COLING/ACL 2006), Sydney, Australia]. We implement this finding to Information Retrieval, as follows. We present a novel automatic query reformulation technique, which is based on shallow syntactic evidence induced from various language samples, and used to enhance the performance of an Information Retrieval system. Firstly, we draw shallow syntactic evidence from language samples of varying size, and compare the effect of language sample size upon retrieval performance, when using our syntactically-based query reformulation (SQR) technique. Secondly, we compare SQR to a state-of-the-art probabilistic pseudo-relevance feedback technique. Additionally, we combine both techniques and evaluate their compatibility. We evaluate our proposed technique across two standard Text REtrieval Conference (TREC) English test collections, and three statistically different weighting models. Experimental results suggest that SQR markedly enhances retrieval performance, and is at least comparable to pseudo-relevance feedback. Notably, the combination of SQR and pseudo-relevance feedback further enhances retrieval performance considerably. These collective experimental results confirm the tenet that high frequency shallow syntactic fragments correspond to content-bearing lexical fragments.