Word association norms, mutual information, and lexicography
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
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Journal of the ACM (JACM)
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Journal of the ACM (JACM)
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Journal of the ACM (JACM)
An information-theoretic approach to automatic query expansion
ACM Transactions on Information Systems (TOIS)
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Proceedings of the 10th international conference on World Wide Web
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Proceedings of the 11th international conference on World Wide Web
Information Retrieval
Focused Crawling Using Context Graphs
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ACM SIGKDD Explorations Newsletter
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Journal of the American Society for Information Science and Technology
The Journal of Machine Learning Research
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EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
ACM SIGIR Forum
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The study of mining the associated words is not new. Because of its wide ranges of applications, it is still an important issue in Information Retrieval. The existing estimators such as joint probability, words association norm do not consider the density of the words present in each window. In this paper, we incorporate the word density and propose estimator based on word density to measure the association between the words. From various experimental results based on the human judgments and precision collected from search engines, we find that the precision of the estimators could be improved by incorporating word density. For all ranges of the size of the windows, our estimator outperforms all other estimators. We also observe that all these estimators (both existing and proposed one) perform relatively better when the windows contain around five sentences. We also show by using Spearman rank-order correlation coefficient that our estimator returns better quality of the ranking of the associated terms.