Chunking, also known as shallow parsing, identifies continuous spans of tokens that form syntactic units such as noun phrases or verb phrases.
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Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Ranked #27 on Named Entity Recognition on CoNLL 2003 (English)
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).
It can also use sentence level tag information thanks to a CRF layer.
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.
Ranked #3 on Grammatical Error Detection on FCE
JointKPE employs a chunking network to identify high-quality phrases and a ranking network to learn their salience in the document.