Adaptation of Relation Extraction Rules to New Domains

This paper presents various strategies for improving the extraction performance of less prominent relations with the help of the rules learned for similar relations, for which large volumes of data are available that exhibit suitable data properties. The rules are learned via a minimally supervised machine learning system for relation extraction called DARE. Starting from semantic seeds, DARE extracts linguistic grammar rules associated with semantic roles from parsed news texts. The performance analysis with respect to different experiment domains shows that the data property plays an important role for DARE. Especially the redundancy of the data and the connectivity of instances and pattern rules have a strong influence on recall. However, most real-world data sets do not possess the desirable small-world property. Therefore, we propose three scenarios to overcome the data property problem of some domains by exploiting a similar domain with better data properties. The first two stra...

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Feiyu Xu, Hans Uszkoreit, Hong Li, Niko Felger
• Keywords: Data Properties, Data Property, Education, LREC 2008, Similar Relations