CRF++

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.

Keywords for this software

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References in zbMATH (referenced in 6 articles )

Showing results 1 to 6 of 6.
Sorted by year (citations)

  1. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  2. Jie Yang; Yue Zhang: NCRF++: An Open-source Neural Sequence Labeling Toolkit (2018) arXiv
  3. Adi, Yossi; Keshet, Joseph: StructED: risk minimization in structured prediction (2016)
  4. 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
  5. 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
  6. Xiong, Ying; Zhu, Jie; Huang, Hao; Xu, Haihua: Minimum tag error for discriminative training of conditional random fields (2009)