C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Text genre classification with genre-revealing and subject-revealing features
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Exploring the use of linguistic features in domain and genre classification
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Automatic detection of text genre
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Recognizing text genres with simple metrics using discriminant analysis
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Genre and domain processing in an information retrieval perspective
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Genre and domain in patent texts
PaIR '10 Proceedings of the 3rd international workshop on Patent information retrieval
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Classification in genres and domains is a major field of research for Information Retrieval (scientific and technical watch, data-mining, etc.) and the selection of appropriate descriptors to characterize and classify texts is particularly crucial to that effect.Most of practical experiments consider that domains are correlated to the content level (words, tokens, lemmas, etc.) and genres to the morphosyntactic or linguistic one (function words, POS, etc.). However, currently used variables are generally not accurate enough to be applied to the categorization task.The present study assesses the impact of the lexical and linguistic levels in the field of genre and domain categorization. The empirical results we obtained demonstrate how important it is to select an appropriate tagset that meets the requirement of the task. The results also assess the efficiency of the linguistic level for both genre- and domain-based categorization.