Learning to compute semantic relatedness using knowledge from wikipedia
|Learning to compute semantic relatedness using knowledge from wikipedia|
|Author(s)||Zheng C., Wang Z., Bie R., Zhou M.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Semantic relatedness, Supervised Learning, Wikipedia (Extra: Artificial intelligence, Computer science, Computers, Baseline methods, Benchmark datasets, Human judgments, Relatedness measures, Semantic relatedness, Supervised learning approaches, Weighted averages, Wikipedia, Supervised learning)|
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Learning to compute semantic relatedness using knowledge from wikipedia is a 2014 conference paper written in English by Zheng C., Wang Z., Bie R., Zhou M. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Recently, Wikipedia has become a very important resource for computing semantic relatedness (SR) between entities. Several approaches have already been proposed to compute SR based on Wikipedia. Most of the existing approaches use certain kinds of information in Wikipedia (e.g. links, categories, and texts) and compute the SR by empirically designed measures. We have observed that these approaches produce very different results for the same entity pair in some cases. Therefore, how to select appropriate features and measures to best approximate the human judgment on SR becomes a challenging problem. In this paper, we propose a supervised learning approach for computing SR between entities based on Wikipedia. Given two entities, our approach first maps entities to articles in Wikipedia; then different kinds of features of the mapped articles are extracted from Wikipedia, which are then combined with different relatedness measures to produce nine raw SR values of the entity pair. A supervised learning algorithm is proposed to learn the optimal weights of different raw SR values. The final SR is computed as the weighted average of raw SRs. Experiments on benchmark datasets show that our approach outperforms baseline methods.
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