Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Multi-way relation classification: application to protein-protein interactions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Detecting Protein-Protein Interactions in Biomedical Texts Using a Parser and Linguistic Resources
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Extracting trees of quantitative serial episodes
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Sequential patterns to discover and characterise biological relations
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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Extraction of named entity relations in textual data is an important challenge in natural language processing. For that purpose, we propose a new data mining approach based on recursive sequence mining. The contribution of this work is twofold. First, we present a method based on a cross-fertilization of sequence mining under constraints and recursive pattern mining to produce a user-manageable set of linguistic information extraction rules. Moreover, unlike most works from the state-of-the-art in natural language processing, our approach does not need syntactic parsing of the sentences neither resource except the training data. Second, we show in practice how to apply the computed rules to detect new relations between named entities, highlighting the interest of hybridization of data mining and natural language processing techniques in the discovery of knowledge. We illustrate our approach with the detection of gene interactions in biomedical literature.