Document Clustering and Ranking System for Exploring MEDLINE Citations

TitleDocument Clustering and Ranking System for Exploring MEDLINE Citations
Publication TypeJournal Article
Year of Publication2007
AuthorsLing Liu, Keke Chen
KeywordsCCPY better ranked important articles than did the others. Furthermore, citation count (CC), i.e., important&#39, including citation count per year (CCPY), our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. <br /> <b>Conclusions</b>: The text mining system studi, ranked the citations in each cluster, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, text summarization, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. <br /> <b>Design</b>: A text mining system framework for automatic document clustering and ra, those articles selected by the Surgical Oncology Society. <br /><b>Results</b>: Our results showed that CCPY outperforms CC and JIF
Abstract

Objective: A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. Design: A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster. Measurements: Several possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as 'important' those articles selected by the Surgical Oncology Society. Results: Our results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. Conclusions: The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.

Full Text

Yongjing Lin, Wenyuan Li, Keke Chen and Ying Liu, ' Document Clustering and Ranking System for Exploring MEDLINE Citations ', Volume 14, Issue 5,2007, Journal of the American Medical Informatics Association (JAMIA). [pdf]
publisher: Journal of the American Medical Informatics Association
year: 2007
hasURL: http://knoesis.wright.edu/library/publications/Keke/JAMIA07.pdf