Improved Method for Relation Adaptation to Extract the New Relations Efficiently from Relational Mapping
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 3
Abstract
Abstract: Traditionally, the web contains different semantic relations. The information extraction focuses on pre-specified request from small set of text. The main task of information retrieval is to extract or organize the information items as well as representation and access storage of items. There are various methods available for relation extraction presented by different authors. The main step we are focusing is supervised relation extraction. The method we propose for relation extraction for adapting new relation with supervised relation extraction system. It is based on three major concepts called domain adaptation, relation extraction and transfer learning. Our proposed method uses combination of under-sampling majority class and oversampling minority class. This paper shows that combination of these two methods improves the classifier performance. We evaluate proposed method for relation extraction using different dataset which contains entities for different relation. Using this method we are going to improve the precision, recall, F-score rate of relations which helps to improve the accuracy of relation those are novel or newly adapted. To overcome challenges in relation extraction that novel entities and relations constantly appear on the web as it contains both structured and unstructured text on the web. Our experimental result shows that the proposed method achieves F-score rate of 69.18. Moreover, it outperforms the numerous methods to adapt new relation efficiently.
Authors and Affiliations
Miss Pragati Sapate , Dr. Sanjay T. Singh
Keywords
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- EP ID EP121431
- DOI 10.9790/0661-16392229
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