Mining the Web for acronyms using the duality of patterns and relations
Proceedings of the 2nd international workshop on Web information and data management
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Semantic relation extraction with kernels over typed dependency trees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Searching patterns for relation extraction over the web: rediscovering the pattern-relation duality
Proceedings of the fourth ACM international conference on Web search and data mining
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Entity relation extraction is mainly focused on researching extraction approaches and improving precision of the extraction results. Although many efforts have been made on this field, there still exist some problems. In order to improve the performance of extracting entity relation, we propose a tuple refinement method based on relationship keyword extension. Firstly, we utilize the diversity of relationships to extend relationship keywords, and then, use the redundancy of network information to extract the second entity based on the principle of proximity and the predefined entity type. Under open web environment, we take four relationships in the experiments and adopt bootstrapping algorithm to acquire the initial tuple set. Three tuple refinement methods are compared: refinement method with threshold set, refinement method with relation extension and refinement method without relation extension. The average F-scores of the experimental results show the proposed method can effectively improve the performance of entity relation extraction.