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
NCRF++: An Open-source Neural Sequence Labeling Toolkit
This paper describes NCRF++, a toolkit for neural sequence labeling.
#5 best model for
Chunking
on Penn Treebank
CHUNKING NAMED ENTITY RECOGNITION (NER) PART-OF-SPEECH TAGGING
Design Challenges and Misconceptions in Neural Sequence Labeling
COLING 2018
• jiesutd/NCRFpp
• 
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
• jiesutd/NCRFpp
•
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
Natural Language Processing (almost) from Scratch
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling.
CHUNKING NAMED ENTITY RECOGNITION (NER) PART-OF-SPEECH TAGGING SEMANTIC ROLE LABELING
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 (NER) 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 (NER) WORD EMBEDDINGS