Little words can make a big difference for text classification
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Projecting corpus-based semantic links on a thesaurus
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Inferring knowledge from a large semantic network
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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This paper presents a versatile system intended to acquire paraphrastic phrases from a representative corpus. In order to decrease the time spent on the elaboration of resources for NLP system (for example Information Extraction, IE hereafter), we suggest to use a knowledge acquisition module that helps extracting new information despite linguistic variation (textual entailment). This knowledge is automatically derived from the text collection, in interaction with a large semantic network.