Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th 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
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Towards the self-annotating web
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Semantic Clustering Using Multiple Ontologies
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
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Knowledge-based data mining and classification algorithms require of systems that are able to extract textual attributes contained in raw text documents, and map them to structured knowledge sources (e.g. ontologies) so that they can be semantically analyzed. The system presented in this paper performs this tasks in an automatic way, relying on a predefined ontology which states the concepts in this the posterior data analysis will be focused. As features, our system focuses on extracting relevant Named Entities from textual resources describing a particular entity. Those are evaluated by means of linguistic and Web-based co-occurrence analyses to map them to ontological concepts, thereby discovering relevant features of the object. The system has been preliminary tested with tourist destinations and Wikipedia textual resources, showing promising results.