Abstract
A measure of term relevancy is important in various applications such as Web search. Although the co-occurrence probability of terms in a database is a simple way to express term relevancy, it suffers from each term having a different co-occurrence tendency. In this paper, we propose a new measure of term relevancy: a ratio of actual and predicted values of co-occurrence (RAP). We construct a model predicting co-occurrence for each query as piecewise approximation lines.
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Notes
- 1.
Hatena Bookmark, http://b.hatena.ne.jp/.
- 2.
MeCab: Yet Another Part-of-Speech and Morphological Analyzer, http://taku910.github.io/mecab/.
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Acknowledgements
This work was partially supported by JSPS KAKENHI Grant Number JP17K00429.
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Koyama, Y., Yumoto, T., Isokawa, T., Kamiura, N. (2019). Measuring Term Relevancy Based on Actual and Predicted Co-occurrence. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_78
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DOI: https://doi.org/10.1007/978-3-030-19063-7_78
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