Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Sequence Data Mining
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Sequential patterns to discover and characterise biological relations
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Detecting apposition for text simplification in basque
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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In this paper, we present a method based on data mining techniques to automatically discover linguistic patterns matching appositive qualifying phrases. We develop an algorithm mining sequential patterns made of itemsets with gap and linguistic constraints. The itemsets allow several kinds of information to be associated with one term. The advantage is the extraction of linguistic patterns with more expressiveness than the usual sequential patterns. In addition, the constraints enable to automatically prune irrelevant patterns. In order to manage the set of generated patterns, we propose a solution based on a partial ordering. A human user can thus easily validate them as relevant linguistic patterns. We illustrate the efficiency of our approach over two corpora coming from a newspaper.