Chunking
118 papers with code • 5 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.
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.
Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
