Chunking
Chunking, also known as shallow parsing, identifies continuous spans of tokens that form syntactic units such as noun phrases or verb phrases.
Example:
| Vinken | , | 61 | years | old |
|---|---|---|---|---|
| B-NLP | I-NP | I-NP | I-NP | I-NP |
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
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Greatest papers with code
Contextual String Embeddings for Sequence Labeling
COLING 2018
• zalandoresearch/flair
• 
Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.
SOTA for Chunking on Penn Treebank
CHUNKING LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS
NCRF++: An Open-source Neural Sequence Labeling Toolkit
ACL 2018
• jiesutd/PyTorchSeqLabel
•
This paper describes NCRF++, a toolkit for neural sequence labeling.
#5 best model for
Chunking
on Penn Treebank
Design Challenges and Misconceptions in Neural Sequence Labeling
COLING 2018
• jiesutd/PyTorchSeqLabel
•
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).
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
Hybrid semi-Markov CRF for Neural Sequence Labeling
ACL 2018
• ZhixiuYe/HSCRF-pytorch
•
This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing.
#23 best model for
Named Entity Recognition
on CoNLL 2003 (English)
CHUNKING DOMAIN ADAPTATION NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS
Semi-supervised Multitask Learning for Sequence Labeling
ACL 2017
• marekrei/sequence-labeler
• 
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.
#3 best model for
Grammatical Error Detection
on FCE
CHUNKING GRAMMATICAL ERROR DETECTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING
Bidirectional LSTM-CRF Models for Sequence Tagging
It can also use sentence level tag information thanks to a CRF layer.
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
The results show that the proposed method achieves as good as or better results compared to the other word embeddings in the tasks we investigate.
CHUNKING DEPENDENCY PARSING NAMED ENTITY RECOGNITION WORD EMBEDDINGS