C4.5: programs for machine learning
C4.5: programs for machine learning
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A vector space model for automatic indexing
Communications of the ACM
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Conditional structure versus conditional estimation in NLP models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Tourism product descriptions are strongly supported on natural language expressions. Appropriate offer selection, according to tourist needs, depends highly on how these are communicated. Since no human interaction is available while presenting tourism products online, the way these are presented, even when using only textual information, is a key success factor for tourism web sites to achieve a purchase. Due to the large amount of tourism offers and the high dynamics in this sector, manual data management is not a reliable or a scalable solution. This paper presents a prototype developed for automatic extraction of relevant knowledge from tourism-related natural language texts. Captured knowledge is represented in a normalized format and new textual descriptions are produced according to available marketing channels. At this phase, the prototype is focused on hotel descriptions and is already using real operational data retrieved from the KEY for Travel tourism platform.