Guest Editors‘ Introduction: Machine Learning and Natural Language
Machine Learning - Special issue on natural language learning
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Towards light semantic processing for Question Answering
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generic parsing for multi-domain semantic interpretation
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Using generalization of syntactic parse trees for taxonomy capture on the web
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Transfer learning of syntactic structures for building taxonomies for search engines
Engineering Applications of Artificial Intelligence
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We define sentence generalization and generalization diagrams as a special sort of conceptual graphs which can be constructed automatically from syntactic parse trees and support semantic classification task. Similarity measure between syntactic parse trees is developed as a generalization operation on the lists of sub-trees of these trees. The diagrams are representation of mapping between the syntactic generalization level and semantic generalization level (anti-unification of logic forms). Generalization diagrams are intended to be more accurate semantic representation than conventional conceptual graphs for individual sentences because only syntactic commonalities are represented at semantic level.