An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Empirical Learning of Natural Language Processing Task
ECML '97 Proceedings of the 9th European Conference on Machine Learning
User-System Cooperation in Document Annotation Based on Information Extraction
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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This paper reports work aimed at developing an open, distributed learning environment, OLLIE, where researchers can experiment with different Machine Learning (ML) methods for Information Extraction. Once the required level of performance is reached, the ML algorithms can be used to speed up the manual annotation process. OLLIE uses a browser client while data storage and ML training is performed on servers. The different ML algorithms use a unified programming interface; the integration of new ones is straightforward.