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Improving The Performance of Triple Generation Based on Distant Supervision By Using Semantic Similarity
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 6,  2016, pp.653-661
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.6.653
 Title & Authors
Improving The Performance of Triple Generation Based on Distant Supervision By Using Semantic Similarity
Yoon, Hee-Geun; Choi, Su Jeong; Park, Seong-Bae;
The existing pattern-based triple generation systems based on distant supervision could be flawed by assumption of distant supervision. For resolving flaw from an excessive assumption, statistics information has been commonly used for measuring confidence of patterns in previous studies. In this study, we proposed a more accurate confidence measure based on semantic similarity between patterns and properties. Unsupervised learning method, word embedding and WordNet-based similarity measures were adopted for learning meaning of words and measuring semantic similarity. For resolving language discordance between patterns and properties, we adopted CCA for aligning bilingual word embedding models and a translation-based approach for a WordNet-based measure. The results of our experiments indicated that the accuracy of triples that are filtered by the semantic similarity-based confidence measure was 16% higher than that of the statistics-based approach. These results suggested that semantic similarity-based confidence measure is more effective than statistics-based approach for generating high quality triples.
triple generation;WordNet;word embedding;semantic similarity;canonical correlation analysis;
 Cited by
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