FlexCRFs is a conditional random field toolkit for segmenting and labeling sequence data written in C/C++ using STL library. It was implemented based on the theoretic model presented in (Lafferty et al. 2001) and (Sha and Pereira 2003). The toolkit uses L-BFGS (Liu and Nocedal 1989) - an advanced convex optimization procedure - to train CRF models. FlexCRFs was designed to deal with hundreds of thousand data sequences and millions of features. FlexCRFs supports both first-order and second-order Markov CRFs. We have tested FlexCRFs on Linux (Red Hat, Fedora, Ubuntu), Sun Solaris, and MS Windows with MS Visual C++.

References in zbMATH (referenced in 2 articles )

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  1. Jie Yang; Yue Zhang: NCRF++: An Open-source Neural Sequence Labeling Toolkit (2018) arXiv
  2. Maes, Francis; Denoyer, Ludovic; Gallinari, Patrick: Structured prediction with reinforcement learning (2009) ioport