Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Document expansion for speech retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 11th international conference on World Wide Web
Word sense disambiguation in information retrieval revisited
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval using word senses: root sense tagging approach
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Word sense disambiguation in queries
Proceedings of the 14th ACM international conference on Information and knowledge management
Language model information retrieval with document expansion
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A comparison of statistical significance tests for information retrieval evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A general optimization framework for smoothing language models on graph structures
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A study on similarity and relatedness using distributional and WordNet-based approaches
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
Smoothing document language model with local word graph
Proceedings of the 18th ACM conference on Information and knowledge management
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
CLEF 2008: ad hoc track overview
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
CLEF 2009 ad hoc track overview: robust-WSD task
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Domain specific data retrieval on the semantic web
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Word sense disambiguation improves information retrieval
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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The use of semantic information to improve IR is a long-standing goal. This paper presents a novel Document Expansion method based on a WordNet-based system to find related concepts and words. Expansion words are indexed separately, and when combined with the regular index, they improve the results in three datasets over a state-of-the-art IR engine. Considering that many IR systems are not robust in the sense that they need careful fine-tuning and optimization of their parameters, we explored some parameter settings. The results show that our method is specially effective for realistic, non-optimal settings, adding robustness to the IR engine. We also explored the effect of document length, and show that our method is specially successful with shorter documents.