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SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
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Improving browsing in digital libraries with keyphrase indexes
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Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Thesaurus based automatic keyphrase indexing
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Keyphrase Extraction Using Semantic Networks Structure Analysis
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Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A concept-based model for enhancing text categorization
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Using lexical chains for keyword extraction
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Wikipedia-Graph Based Key Concept Extraction towards News Analysis
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
The automatic creation of literature abstracts
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MaxMatcher: biological concept extraction using approximate dictionary lookup
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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Scientific publications are often associated with a set of keywords to describe their content. Automating the process of keyword extraction and assignment could be useful in indexing electronic documents and building digital libraries. In this paper we propose a new approach to biomedical Concept Extraction CE using semantic features of concept graphs. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. We adopt concept relation weights to improve the ranking process of potential key concepts. We perform both objective and human-based subjective evaluations. The results show that using relation weights significantly improves the performance of CE. The results also highlight the subjectivity of the CE procedure as well as of its evaluation.