CRF++: Yet Another CRF toolkit. CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. CRF++ is designed for generic purpose and will be applied to a variety of NLP tasks, such as Named Entity Recognition, Information Extraction and Text Chunking.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Adi, Yossi; Keshet, Joseph: StructED: risk minimization in structured prediction (2016)
- Hsu, Chun-Nan; Huang, Han-Shen; Chang, Yu-Ming; Lee, Yuh-Jye: Periodic step-size adaptation in second-order gradient descent for single-pass on-line structured learning (2009) ioport
- Huang, Han-Shen; Yang, Bo-Hou; Chang, Yu-Ming; Hsu, Chun-Nan: Global and componentwise extrapolations for accelerating training of Bayesian networks and conditional random fields (2009) ioport
- Xiong, Ying; Zhu, Jie; Huang, Hao; Xu, Haihua: Minimum tag error for discriminative training of conditional random fields (2009)