Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Language-Independent Approach to European Text Retrieval
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Named entity recognition for Catalan using Spanish resources
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Proceedings of the 3rd international conference on Knowledge capture
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A stacked, voted, stacked model for named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
STEWARD: architecture of a spatio-textual search engine
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Proceedings of the 2008 ACM symposium on Applied computing
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Simplified feature set for Arabic named entity recognition
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Extracting named entities using support vector machines
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Appropriate kernel functions for support vector machine learning with sequences of symbolic data
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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We describe a named-entity tagging system that requires minimal linguistic knowledge and thus may be applied to new target languages without significant adaptation. To maintain a language- neutral posture, the system is linguistically nave, and in fact, reduces the tagging problem to supervised machine learning. A large number of binary features are extracted from labeled data to train classifiers and computationally expensive features are eschewed. We have initially focused our attention on linear support vectors machines (SVMs); SVMs are known to work well when a large number of features is used as long as the individual vectors are sparse. We call our system SNOOD (Hopkins APL Inductive Retargetable Named Entity Tagger).