Abstract
The related applications are limited due to the static characteristics on existing relatedness calculation algorithms. We proposed a method aiming to efficiently compute the dynamic relatedness between Chinese entity-pairs, which changes over time. Our method consists of three components: using co-occurrence statistics method to mine the co-occurrence information of entities from the news texts, inducing the development law of dynamic relatedness between entity-pairs, taking the development law as basis and consulting the existing relatedness measures to design a dynamic relatedness measure algorithm. We evaluate the proposed method on the relatedness value and related entity ranking. Experimental results on a dynamic news corpus covering seven domains show a statistically significant improvement over the classical relatedness measure.
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Wang, Z., Yang, J., Lin, X. (2012). Measuring the Dynamic Relatedness between Chinese Entities Orienting to News Corpus. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_49
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DOI: https://doi.org/10.1007/978-3-642-31537-4_49
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