Neural Models for Sequence Chunking
Table 2: Comparison with published results on the CoNLL chunking dataset.
['Methods', 'F1-score']
\citeauthorhuang2015bidirectional used a BiLSTM-CRF framework together with a lot of handcraft features. \citeauthoryang2016multi extend this framework and employ a GRU to incorporate the character information of words, rather than using handcrafted features. To our best knowledge, they got the current best results 94.66 on the text chunking task. without using CRF, we get a state-of-the-art performance of 94.72.