Foundations of statistical natural language processing
Foundations of statistical natural language processing
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Kernel methods for relation extraction
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Efficient set joins on similarity predicates
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
POLYPHONET: an advanced social network extraction system from the web
Proceedings of the 15th international conference on World Wide Web
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Approximation algorithms for co-clustering
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Measuring the similarity between implicit semantic relations from the web
Proceedings of the 18th international conference on World wide web
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Shallow semantics for relation extraction
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Exploiting macro and micro relations toward web intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Proceedings of the 20th international conference companion on World wide web
Enishi: searching knowledge about relations by complementarily utilizing wikipedia and the web
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Combining the Best of Two Worlds: NLP and IR for Intranet Search
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Extracting multi-dimensional relations: a generative model of groups of entities in a corpus
Proceedings of the 20th ACM international conference on Information and knowledge management
Structured relation discovery using generative models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Relation adaptation: learning to extract novel relations with minimum supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Cross-Language Latent Relational Search between Japanese and English Languages Using a Web Corpus
ACM Transactions on Asian Language Information Processing (TALIP)
User-driven relational models for entity-relation search and extraction
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
Unsupervised relation discovery with sense disambiguation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Pattern learning for relation extraction with a hierarchical topic model
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
KrakeN: N-ary facts in open information extraction
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
Tuple refinement method based on relationship keyword extension
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Extracting semantic relations among entities is an important first step in various tasks in Web mining and natural language processing such as information extraction, relation detection, and social network mining. A relation can be expressed extensionally by stating all the instances of that relation or intensionally by defining all the paraphrases of that relation. For example, consider the ACQUISITION relation between two companies. An extensional definition of ACQUISITION contains all pairs of companies in which one company is acquired by another (e.g. (YouTube, Google) or (Powerset, Microsoft)). On the other hand we can intensionally define ACQUISITION as the relation described by lexical patterns such as X is acquired by Y, or Y purchased X, where X and Y denote two companies. We use this dual representation of semantic relations to propose a novel sequential co-clustering algorithm that can extract numerous relations efficiently from unlabeled data. We provide an efficient heuristic to find the parameters of the proposed coclustering algorithm. Using the clusters produced by the algorithm, we train an L1 regularized logistic regression model to identify the representative patterns that describe the relation expressed by each cluster. We evaluate the proposed method in three different tasks: measuring relational similarity between entity pairs, open information extraction (Open IE), and classifying relations in a social network system. Experiments conducted using a benchmark dataset show that the proposed method improves existing relational similarity measures. Moreover, the proposed method significantly outperforms the current state-of-the-art Open IE systems in terms of both precision and recall. The proposed method correctly classifies 53 relation types in an online social network containing 470; 671 nodes and 35; 652; 475 edges, thereby demonstrating its efficacy in real-world relation detection tasks.