Ontology Learning and Its Application to Automated Terminology Translation
IEEE Intelligent Systems
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Shallow parsing with conditional random fields
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
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Collective annotation of Wikipedia entities in web text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a pipelined supervised learning approach named SDOI to the task of interlinking the concepts mentioned within a document to the concepts within an ontology. Concept mention identification is performed by training a sequential tagging model. Each identified concept mention is then associated with a set of candidate ontology concepts along with a feature vector based on features proposed in the literature and novel ones based on new data sources, such as from the training corpus itself. An iterative algorithm is defined for handling collective features. We show a lift in performance over applicable baselines against the ability to identify the concept mentions within the 139 KDD-2009 conference paper abstracts, and to link these concept mentions to a domain-specific ontology for the field of data mining. Additional experiments of 22 ICDM-2009 abstracts suggest that the trained models are portable both in terms of accuracy and in their ability to reduce annotation time.