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Papers/NCRF++: An Open-source Neural Sequence Labeling Toolkit

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang

2018-06-14ACL 2018 7Part-Of-Speech TaggingNamed Entity Recognition (NER)Chunking
PaperPDFCodeCode(official)Code

Abstract

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.

Results

TaskDatasetMetricValueModel
Part-Of-Speech TaggingPenn TreebankAccuracy97.49NCRF++
Named Entity Recognition (NER)CoNLL 2003 (English)F191.35NCRF++
ChunkingPenn TreebankF1 score95.06NCRF++
Shallow SyntaxPenn TreebankF1 score95.06NCRF++

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