Fundamentals of speech recognition
Fundamentals of speech recognition
Information Retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Language independent morphological analysis
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A knowledge-free method for capitalized word disambiguation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
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Subject headings systems are tools for organization of knowledge that have been developed over the years by libraries. The SKOS Simple Knowledge Organization System has provided a practical way to represent subject headings systems using the Resource Description Framework, and several libraries have taken the initiative to make subject headings systems widely available as open linked data. Each individual subject heading describes a concept, however, in the majority of cases, one subject heading is actually a combination of several concepts, such as a topic bounded in geographical and temporal scopes. In these cases, the label of the concept actually carries several concepts which are not represented in structured form. Our work explores machine learning techniques to recognize the sub concepts represented in the labels of SKOS subject headings. This paper describes a language independent named entity recognition technique based on conditional random fields, a machine learning algorithm for sequence labelling. This technique was evaluated on a subset of the Library of Congress Subject Headings, where we measured the recognition of geographic concepts, topics, time periods and historical periods. Our technique achieved an overall F1 score of 0.98.