Class-based n-gram models of natural language
Computational Linguistics
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
RelEx---Relation extraction using dependency parse trees
Bioinformatics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Improving semi-supervised acquisition of relation extraction patterns
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Multi-task transfer learning for weakly-supervised relation extraction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Exploiting background knowledge for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A combination of topic models with max-margin learning for relation detection
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Compensating for annotation errors in training a relation extractor
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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In this paper, we observe that there exists a second dimension to the relation extraction (RE) problem that is orthogonal to the relation type dimension. We show that most of these second dimensional structures are relatively constrained and not difficult to identify. We propose a novel algorithmic approach to RE that starts by first identifying these structures and then, within these, identifying the semantic type of the relation. In the real RE problem where relation arguments need to be identified, exploiting these structures also allows reducing pipelined propagated errors. We show that this RE framework provides significant improvement in RE performance.