Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Semantic computation in a Chinese question-answering system
Journal of Computer Science and Technology
REES: a large-scale relation and event extraction system
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
HHMM-based Chinese lexical analyzer ICTCLAS
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Relation extraction using label propagation based semi-supervised learning
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A probabilistic model of redundancy in information extraction
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Evaluation of contextual information retrieval effectiveness: overview of issues and research
Knowledge and Information Systems
Improving semantic role labeling with word sense
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Knowledge-based vector space model for text clustering
Knowledge and Information Systems
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Building a highly-compact and accurate associative classifier
Applied Intelligence
An information gain-based approach for recommending useful product reviews
Knowledge and Information Systems
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
Case learning for CBR-based collision avoidance systems
Applied Intelligence
Discriminative Feature Selection by Nonparametric Bayes Error Minimization
IEEE Transactions on Knowledge and Data Engineering
Learning to adapt cross language information extraction wrapper
Applied Intelligence
Hi-index | 0.00 |
Named entity relations are a foundation of semantic networks, ontology and the semantic Web, and are widely used in information retrieval and machine translation, as well as automatic question and answering systems. In named entity relations, relational feature selection and extraction are two key issues. The location features possess excellent computability and operability, while the semantic features have strong intelligibility and reality. Currently, relation extraction of Chinese named entities mainly adopts the Vector Space Model (VSM), a traditional semantic computing or the classification method, and these three methods use either the location features or the semantic features alone, resulting in unsatisfactory extraction. A relation extraction method of Chinese named entities called LaSE is proposed to combine the information gain of the positions of words and semantic computing based on HowNet. LaSE is scalable, semi-supervised and domain independent. Extensive experiments show that LaSE is superior, with an F-score of 0.879, which is at least 0.113 better than existing extraction methods that use either the location features or the semantic features alone.