Foundations of statistical natural language processing
Foundations of statistical natural language processing
A symbolic and surgical acquisition of terms through variation
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Text Mining for Causal Relations
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Unsupervised discovery of scenario-level patterns for Information Extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning semantic constraints for the automatic discovery of part-whole relations
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A language model approach to keyphrase extraction
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Two Stage Semantic Relation Extraction
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semi-supervised learning for semantic relation classification using stratified sampling strategy
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
RelExt: a tool for relation extraction from text in ontology extension
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Aircraft interior failure pattern recognition utilizing text mining and neural networks
Journal of Intelligent Information Systems
Discovering semantic relations using prepositional phrases
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Concept map construction from text documents using affinity propagation
Journal of Information Science
| Hi-index | 0.00 |
Ontology of a domain mainly consists of concepts, taxonomical (hierarchical) relations and non-taxonomical relations. Automatic ontology construction requires methods for extracting both taxonomical and non-taxonomical relations. Compared to extensive works on concept extraction and taxonomical relation learning, little attention has been given on identification and labeling of non-taxonomical relations in text mining. In this paper, we propose an unsupervised technique for extracting non-taxonomical relations from domain texts. We propose the VF*ICF metric for measuring the importance of a verb as a representative relation label, in much the same spirit as the TF*IDF measure in information retrieval. Domain-relevant concepts (nouns) are extracted using techniques developed earlier. Candidate non-taxonomical relations are generated as (SVO) triples of the form (subject, verb, object) from domain texts. A statistical method with log-likelihood ratios is used to estimate the significance of relationships between concepts and to select suitable relation labels. Texts from two domains, the Electronic Voting (EV) domain texts and the Tenders and Mergers (TNM) domain texts are used to compare our method with one of the existing approaches. Experiments showed that our method achieved better performance in both domains.