Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Coupling semi-supervised learning of categories and relations
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Collective intelligence as a source for machine learning self-supervision
Proceedings of the 4th International Workshop on Web Intelligence & Communities
Discovering and exploring relations on the web
Proceedings of the VLDB Endowment
Instance-driven attachment of semantic annotations over conceptual hierarchies
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
PATTY: a taxonomy of relational patterns with semantic types
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Discovering relations using matrix factorization methods
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Classifying entities into an incomplete ontology
Proceedings of the 2013 workshop on Automated knowledge base construction
Coupling as Strategy for Reducing Concept-Drift in Never-ending Learning Environments
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
| Hi-index | 0.00 |
Traditional approaches to Relation Extraction from text require manually defining the relations to be extracted. We propose here an approach to automatically discovering relevant relations, given a large text corpus plus an initial ontology defining hundreds of noun categories (e.g., Athlete, Musician, Instrument). Our approach discovers frequently stated relations between pairs of these categories, using a two step process. For each pair of categories (e.g., Musician and Instrument) it first co-clusters the text contexts that connect known instances of the two categories, generating a candidate relation for each resulting cluster. It then applies a trained classifier to determine which of these candidate relations is semantically valid. Our experiments apply this to a text corpus containing approximately 200 million web pages and an ontology containing 122 categories from the NELL system [Carlson et al., 2010b], producing a set of 781 proposed candidate relations, approximately half of which are semantically valid. We conclude this is a useful approach to semi-automatic extension of the ontology for large-scale information extraction systems such as NELL.