Modern Information Retrieval
Kernel methods for relation extraction
The Journal of Machine Learning Research
Focused named entity recognition using machine learning
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Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
Computational Linguistics
Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the 16th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
Structured entity identification and document categorization: two tasks with one joint model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Empirical study on the performance stability of named entity recognition model across domains
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Domain adaptation with latent semantic association for named entity recognition
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
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Proceedings of the 18th ACM conference on Information and knowledge management
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This paper describes EagleEye, which is an intelligent system that provides business intelligence through advanced data mining and text analytics. Unlike traditional search engines, EagleEye is entity oriented, and an entity can be an organization, a person, or a place. Given an entity name, the basic function of EagleEye is to generate a consolidated view of the entity information it gathers from many disparate data sources and to organize and categorize it, and automatically detect entity relationships. EagleEye can also analyze the opinions of entities, evaluate whether they are positive or negative, and provide insight into many aspects of consumer sentiment toward product brands. This type of information can enable enterprises to manage the reputation of their brands and to respond more quickly to changes in the marketplace. We present the key technologies--such as entity-name grouping, entity-relation extraction, and entity-oriented opinion mining--that were developed to support these functions. EagleEye has been successfully deployed to a number of clients across a variety of industries in China. Several case studies are presented to demonstrate in practice the capability and business value of EagleEye.