Hybrid semi-Markov CRF for Neural Sequence Labeling
This is an incremental improvement for natural language processing tasks like named entity recognition.
The paper tackled neural sequence labeling by proposing hybrid semi-Markov CRFs that integrate word-level and segment-level information, achieving state-of-the-art performance on the CoNLL 2003 NER task without external knowledge.
This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random fields (CRFs), SCRFs have been designed for the tasks of assigning labels to segments by extracting features from and describing transitions between segments instead of words. In this paper, we improve the existing SCRF methods by employing word-level and segment-level information simultaneously. First, word-level labels are utilized to derive the segment scores in SCRFs. Second, a CRF output layer and an SCRF output layer are integrated into an unified neural network and trained jointly. Experimental results on CoNLL 2003 named entity recognition (NER) shared task show that our model achieves state-of-the-art performance when no external knowledge is used.