C4.5: programs for machine learning
C4.5: programs for machine learning
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Morphological rule induction for terminology acquistion
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Natural Language Engineering
Automatic identification of word translations from unrelated English and German corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Looking for candidate translational equivalents in specialized, comparable corpora
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 2
An approach based on multilingual thesauri and model combination for bilingual lexicon extraction
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Large linguistically-processed web corpora for multiple languages
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Brains, not brawn: The use of “smart” comparable corpora in bilingual terminology mining
ACM Transactions on Speech and Language Processing (TSLP)
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We present in this paper the development of a specialized comparable corpora compilation tool, for which quality would be close to a manually compiled corpus. The comparability is based on three levels: domain, topic and type of discourse. Domain and topic can be filtered with the keywords used through web search. But the detection of the type of discourse needs a wide linguistic analysis. The first step of our work is to automate the detection of the type of discourse that can be found in a scientific domain (science and popular science) in French and Japanese languages. First, a contrastive stylistic analysis of the two types of discourse is done on both languages. This analysis leads to the creation of a reusable, generic and robust typology. Machine learning algorithms are then applied to the typology, using shallow parsing. We obtain good results, with an average precision of 80% and an average recall of 70% that demonstrate the efficiency of this typology. This classification tool is then inserted in a corpus compilation tool which is a text collection treatment chain realized through IBM UIMA system. Starting from two specialized web documents collection in French and Japanese, this tool creates the corresponding corpus.