Chunking

30 papers with code · Natural Language Processing

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

Leaderboards

Latest papers with code

Joint Keyphrase Chunking and Salience Ranking with BERT

28 Apr 2020thunlp/BERT-KPE

JointKPE employs a chunking network to identify high-quality phrases and a ranking network to learn their salience in the document.

CHUNKING

66
28 Apr 2020

Language-Agnostic Syllabification with Neural Sequence Labeling

29 Sep 2019jacobkrantz/lstm-syllabify

The concept of the syllable is cross-linguistic, though formal definitions are rarely agreed upon, even within a language.

CHUNKING NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING PART-OF-SPEECH TAGGING SPEECH RECOGNITION

7
29 Sep 2019

Gated Task Interaction Framework for Multi-task Sequence Tagging

29 Sep 2019kaeflint/GTI

Others have shown that linguistic features can improve the performance of neural models on tasks such as chunking and named entity recognition (NER).

CHUNKING MULTI-TASK LEARNING NAMED ENTITY RECOGNITION

0
29 Sep 2019

Augmenting Neural Networks with First-order Logic

ACL 2019 utahnlp/layer_augmentation

Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset.

CHUNKING NATURAL LANGUAGE INFERENCE READING COMPREHENSION

22
14 Jun 2019

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling

ACL 2019 Adaxry/GCDT

Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs).

#2 best model for Named Entity Recognition on CoNLL 2003 (English) (using extra training data)

CHUNKING NAMED ENTITY RECOGNITION WORD EMBEDDINGS

47
06 Jun 2019

Contextual String Embeddings for Sequence Labeling

COLING 2018 zalandoresearch/flair

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.

CHUNKING LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS

8,641
01 Aug 2018

Hybrid semi-Markov CRF for Neural Sequence Labeling

ACL 2018 ZhixiuYe/HSCRF-pytorch

This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing.

CHUNKING DOMAIN ADAPTATION NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS

262
01 Jul 2018

Design Challenges and Misconceptions in Neural Sequence Labeling

COLING 2018 jiesutd/PyTorchSeqLabel

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).

CHUNKING

1,523
12 Jun 2018