Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
An improved hidden Markov model for literature metadata extraction
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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Metadata serves as an important role in the archiving, management and sharing of the scientific literatures. It consists of title, authors, affiliation, address, email, abstract, keywords, etc. However, the metadata is usually easy-to-read for human and difficult-to-recognize for computers. In this paper, we propose to improve Viterbi algorithm based on text blocks instead of words, increase the precision and recall based on unique characteristics of metadata items.