Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Semantic Information Processing
Semantic Information Processing
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Exploiting macro and micro relations toward web intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Confronting the challenges of annotating naturally occurring text into a semantically structured form to facilitate automatic information extraction, current Semantic Role Labeling (SRL) systems have been specifically examining a semantic predicate-argument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which is intended to describe the concept structure of text using a set of pre-defined semantic relations, we develop a parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. With the assumption that all relation instances are detected, we present a relation classification approach facing the challenges of CDL.nl relation extraction. Preliminary evaluation on a manual dataset, using Support Vector Machine, shows that CDL.nl relations can be classified with good performance.