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Kang, I.-S.; Na, S.-H.; Lee, S.; Jung, H.; Kim, P.; Sung, W.-K.; Lee, J.-H.: On co-authorship for author disambiguation (2009)
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Na, S.-H.; Kang, I.-S.; Lee, J.-H.: Adaptive document clustering based on query-based similarity (2007)
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- Abstract
- In information retrieval, cluster-based retrieval is a well-known attempt in resolving the problem of term mismatch. Clustering requires similarity information between the documents, which is difficult to calculate at a feasible time. The adaptive document clustering scheme has been investigated by researchers to resolve this problem. However, its theoretical viewpoint has not been fully discovered. In this regard, we provide a conceptual viewpoint of the adaptive document clustering based on query-based similarities, by regarding the user's query as a concept. As a result, adaptive document clustering scheme can be viewed as an approximation of this similarity. Based on this idea, we derive three new query-based similarity measures in language modeling framework, and evaluate them in the context of cluster-based retrieval, comparing with K-means clustering and full document expansion. Evaluation result shows that retrievals based on query-based similarities significantly improve the baseline, while being comparable to other methods. This implies that the newly developed query-based similarities become feasible criterions for adaptive document clustering.
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Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007)
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Kang, I.-S.; Na, S.-H.; Kim, J.; Lee, J.-H.: Cluster-based patent retrieval (2007)
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Na, S.-H.; Kang, I.-S.; Lee, J.-H.: Parsimonious translation models for information retrieval (2007)
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Lee, J.-H.; Park, S.; Ahn, C.-M.; Kim, D.: Automatic generic document summarization based on non-negative matrix factorization (2009)
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Jett, J.; Humpal, N.; Charles, V.; Lee, J.-H.: What is a series, really? (2017)
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