Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Kernel methods for relation extraction
The Journal of Machine Learning Research
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Introduction to Information Retrieval
Introduction to Information Retrieval
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Distant supervision for relation extraction without labeled data
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
Incorporating global information into named entity recognition systems using relational context
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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This work investigates clustering techniques for Relation Extraction (RE). Relation Extraction is the task of extracting relationships among named entities (e.g., people, organizations and geo-political entities) from natural language text. We are particularly interested in the open RE scenario, where the number of target relations is too large or even unknown. Our contributions are in two aspects of the clustering process: (1) extraction and weighting of features and (2) scalability. In order to evaluate our techniques in large scale, we propose an automatic evaluation method based on pointwise mutual information. Our preliminary results show that our clustering techniques as well as our evaluation method are promising.