New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Learning for text categorization and information extraction with ILP
Learning language in logic
The Perceptron Algorithm with Uneven Margins
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Evolving GATE to meet new challenges in language engineering
Natural Language Engineering
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Open information extraction from the web
Communications of the ACM - Surviving the data deluge
GoodRelations: An Ontology for Describing Products and Services Offers on the Web
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
ADVCOMP '08 Proceedings of the 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences
Adapting svm for data sparseness and imbalance: A case study in information extraction
Natural Language Engineering
Event extraction from trimmed dependency graphs
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
TectoMT: highly modular MT system with tectogrammatics used as transfer layer
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Recognizing textual entailment using sentence similarity based on dependency tree skeletons
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Using ILP to construct features for information extraction from semi-structured text
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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In this paper we present a method for semantic annotation of texts, which is based on a deep linguistic analysis (DLA) and Inductive Logic Programming (ILP). The combination of DLA and ILP have following benefits: Manual selection of learning features is not needed. The learning procedure has full available linguistic information at its disposal and it is capable to select relevant parts itself. Learned extraction rules can be easily visualized, understood and adapted by human. A description, implementation and initial evaluation of the method are the main contributions of the paper.