Abstract
Traditional search technologies are based on similarity relationship such that they return content similar documents in accordance with a given one. However, such similarity-based search does not always result in good results, e.g., similar documents will bring little additional information so that it is difficult to increase information gain. In this paper, we propose a method to find similar but different documents of a user-given one by distinguishing coordinate relationship from similarity relationship between documents. Simply, a similar but different document denotes the document with the same topic as that of the given document, but describing different events or concepts. For example, given as the input a news article stating the occurrence of the Oregon school shooting, articles stating the occurrence of other school shooting events, such as the Virginia Tech shooting, are detected and returned to users. Experiments conducted on the New York Times Annotated Corpus verify the effectiveness of our method and illustrate the importance of incorporating coordinate relationship to find similar but different documents.
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Notes
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Follow-up is an article giving further information on a previously reported news event.
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Note that verbs are compared in their base form.
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Acknowledgment
This work was supported in part by the following projects: Grants-in-Aid for Scientific Research (Nos. 16H02906, 15H01718 and 24680008) from MEXT of Japan.
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Zhao, M., Ohshima, H., Tanaka, K. (2016). Finding “Similar but Different” Documents Based on Coordinate Relationship. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham. https://doi.org/10.1007/978-3-319-49304-6_15
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