Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Clustering for unsupervised relation identification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical 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
Extracting information networks from the blogosphere
ACM Transactions on the Web (TWEB)
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We study the problem of extracting all possible relations among named entities from unstructured text, a task known as Open Information Extraction (Open IE). A state-of-the-art Open IE system consists of natural language processing tools to identify entities and extract sentences that relate such entities, followed by using text clustering to identify the relations among co-occurring entity pairs. In particular, we study how the current weighting scheme used for Open IE affects the clustering results and propose a term weighting scheme that significantly improves on the state-of-the-art in the task of relation extraction both when used in conjunction with the standard tf. idf scheme, and also when used as a pruning filter.