SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
A New Semantic Representation for Short Texts
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Word Sense Disambiguation: Algorithms and Applications
Word Sense Disambiguation: Algorithms and Applications
Enhancing short text retrieval in databases
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Semantic processing of database textual attributes using wikipedia
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
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Textual attributes in relational databases contain useful information that sometimes is not handled in a proper way. Syntactic querying of textual fields provides only superficial information, avoiding issues related to the semantics of data. In order to extract relevant information from these fields, a text mining process is conducted. The AP-Sets mining structure allows the building of an intermediate representation for texts based on the Apriori property. This intermediate form has the shape of a lattice of text itemsets, and can be translated into a basic ontology using semantic relations extracted from WordNet lexical database. The ontology generated can be seen as a summarized structured description of the contents of the textual attribute. Furthermore, it adds semantic capabilities to the query process on textual attributes.