Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
On the Positive--Negative Partial Set Cover problem
Information Processing Letters
IEEE Transactions on Knowledge and Data Engineering
Optimal Boolean Matrix Decomposition: Application to Role Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
On the Complexity of Nonnegative Matrix Factorization
SIAM Journal on Optimization
Model order selection for boolean matrix factorization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering relations between noun categories
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Extracting information networks from the blogosphere
ACM Transactions on the Web (TWEB)
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
| Hi-index | 0.00 |
Traditional relation extraction methods work on manually defined relations and typically expect manually labelled extraction patterns for each relation. This strongly limits the scalability of these systems. In Open Relation Extraction (ORE), the relations are identified automatically based on co-occurrences of "surface relations" (contexts) and entity pairs. The recently-proposed methods for ORE use partition clustering to find the relations. In this work we propose the use of matrix factorization methods instead of clustering. Specifically, we study Non-Negative Matrix Factorization (NMF) and Boolean Matrix Factorization (BMF). These methods overcome many problems inherent in clustering and perform better than the k-means clustering in our evaluation.