Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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Information extraction from research papers using conditional random fields
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In this paper, we deal with the problem of extracting and processing useful information from bibliographic references in Digital Humanities (DH) data. A machine learning technique for sequential data analysis, Conditional Random Field is applied to a corpus extracted from OpenEdition site, a web platform for journals and book collections in the humanities and social sciences. We present our ongoing project with this purpose that includes the construction of a proper corpus and a efficient CRF model on this as a preliminary. This project is supported by Google Grant for Digital Humanities. A number of experiments are conducted to find one of the best settings for a CRF model on the corpus, and we verify them both in an automatic and manual way of evaluation.