Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
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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.