Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
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We explore the use of conditional random fields (CRFs) to automatically extract important metadata from clinical research articles. These metadata fields include formulaic meta-data about the authors, extracted from the title page, as well as free text fields concerning the study's critical parameters, such as longitudinal variables and medical intervention methods, extracted from the body text of the article. Extracting such information can help both readers conduct deep semantic search of articles and policy makers and sociologists track macro level trends in research. Preliminary results show an acceptable level of performance for formulaic metadata and a high precision for those found in the free text.