Chunking
67 papers with code • 6 benchmarks • 5 datasets
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 |
Benchmarks
These leaderboards are used to track progress in Chunking
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Chunking models and implementations
Penn Treebank
CoNLL 2003
Most implemented papers
Bidirectional LSTM-CRF Models for Sequence Tagging
It can also use sentence level tag information thanks to a CRF layer.
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
UKPLab/emnlp2017-bilstm-cnn-crf
•
•
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
Semi-supervised Multitask Learning for Sequence Labeling
marekrei/sequence-labeler
•
•
ACL 2017
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.
NCRF++: An Open-source Neural Sequence Labeling Toolkit
This paper describes NCRF++, a toolkit for neural sequence labeling.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
modelscope/adaseq
•
•
ACL 2021
We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.
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.
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks.
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Design Challenges and Misconceptions in Neural Sequence Labeling
jiesutd/NCRFpp
•
•
COLING 2018
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).
Capturing Global Informativeness in Open Domain Keyphrase Extraction
thunlp/BERT-KPE
•
•
Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e. g., web pages with variant domains and qualities.
