151 papers with code • 1 benchmarks • 1 datasets
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Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing.
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.
Ranked #5 on Named Entity Recognition on CoNLL++
We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i. e. NER, Chunking, and POS tagging).
Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks.
Ranked #9 on Part-Of-Speech Tagging on Penn Treebank
The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in CWS and POS.