Description:

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.

Features

• Can redefine feature sets
• Written in C++ with STL
• Fast training based on LBFGS, a quasi-newton algorithm for large scale numerical optimization problem
• Less memory usage both in training and testing encoding/decoding in practical time
• Can perform n-best outputs
• Can perform single-best MIRA training
• Can output marginal probabilities for all candidates
• Available as an open source software

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Posix

Data Formats: Ascii

Tags: Crf, Segmentation, Conditional Random Fields, Lbfgs, Named Entity Recognition, Nlp

Archive: download here

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