Papers with Code · CC BY-SA 4.0

Chunking

Shallow Syntax › Chunking 24 평가 기록 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

논문

Automated Concatenation of Embeddings for Structured Prediction

2010.05006 2020-10-10 이 페이지의 평가 4건

F1: 95.0

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network

2009.08229 2020-09-17 이 페이지의 평가 2건

F1: 94.04

Contextual String Embeddings for Sequence Labeling

2018-08-01 이 페이지의 평가 2건

F1 score: 96.72

Cross-View Training for Semi-Supervised Learning

2018-01-01 이 페이지의 평가 2건

Exact Span F1: 96.98

More Embeddings, Better Sequence Labelers?

2009.08330 2020-09-17 이 페이지의 평가 2건

F1: 94.4

Robust Multilingual Part-of-Speech Tagging via Adversarial Training

1711.04903 2017-11-14 이 페이지의 평가 2건

Exact Span F1: 95.25

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

1611.01587 2016-11-05 이 페이지의 평가 1건

F1 score: 95.77

Bidirectional LSTM-CRF Models for Sequence Tagging

1508.01991 2015-08-09 이 페이지의 평가 1건

F1 score: 94.46

Deep multi-task learning with low level tasks supervised at lower layers

2016-08-01 이 페이지의 평가 1건

F1 score: 95.57

Def2Vec: Extensible Word Embeddings from Dictionary Definitions

2023-12-16 이 페이지의 평가 1건

Accuracy: 77.69F1: 81.45Precision: 86.56Recall: 77.69

Enhance Robustness of Sequence Labelling with Masked Adversarial Training

2020-11-01 이 페이지의 평가 1건

Exact Span F1: 97.04

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

2105.03654 2021-05-08 이 페이지의 평가 1건

Exact Span F1: 97.18

Learning Better Internal Structure of Words for Sequence Labeling

1810.12443 2018-10-29 이 페이지의 평가 1건

F1 score: 95.29

NCRF++: An Open-source Neural Sequence Labeling Toolkit

1806.05626 2018-06-14 이 페이지의 평가 1건

F1 score: 95.06

SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling

2020-07-01 이 페이지의 평가 1건

Exact Span F1: 95.45