The nature of statistical learning theory
The nature of statistical learning theory
An empirical study of automated dictionary construction for information extraction in three domains
Artificial Intelligence - Special volume on empirical methods
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Information extraction from HTML: application of a general machine learning approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction
IEEE Transactions on Knowledge and Data Engineering
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Adaptive sentence boundary disambiguation
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
TEG: a hybrid approach to information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Adaptive information extraction
ACM Computing Surveys (CSUR)
A modular information extraction system
Intelligent Data Analysis
Syntactic Extraction Approach to Processing Local Document Collections
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
A systematic comparison of feature-rich probabilistic classifiers for NER tasks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Where do I start?: algorithmic strategies to guide intelligence analysts
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
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Information extraction (IE) is of great importance in many applications including web intelligence, search engines, text understanding, etc. To extract information from text documents, most IE systems rely on a set of extraction patterns. Each extraction pattern is defined based on the syntactic and/or semantic constraints on the positions of desired entities within natural language sentences. The IE systems also provide a set of pattern templates that determines the kind of syntactic and semantic constraints to be considered. In this paper, we argue that such pattern templates restricts the kind of extraction patterns that can be learned by IE systems. To allow a wider range of context information to be considered in learning extraction patterns, we first propose to model the content and context information of a candidate entity to be extracted as a set of features. A classification model is then built for each category of entities using Support Vector Machines (SVM). We have conducted IE experiments to evaluate our proposed method on a text collection in the terrorism domain. From the preliminary experimental results, we conclude that our proposed method can deliver reasonable accuracies.