68 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.


Vinken , 61 years old


Use these libraries to find Chunking models and implementations
3 papers
2 papers

Most implemented papers

Bidirectional LSTM-CRF Models for Sequence Tagging

determined22/zh-ner-tf 9 Aug 2015

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 21 Jul 2017

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

jiesutd/NCRFpp ACL 2018

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

faramarzmunshi/d2l-nlp 2 Mar 2011

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

hassyGo/charNgram2vec EMNLP 2017

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

flairNLP/flair ACL 2017

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 28 Apr 2020

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.