End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Xuezhe Ma; Eduard Hovy

doi:https://doi.org/10.18653/v1/p16-1101

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

2016 · 2,595 citations · 2 from inside this corpus

State-of-the-art sequence labeling systems traditionally require large amounts of taskspecific knowledge in the form of handcrafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word-and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data preprocessing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks -Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets -97.55% accuracy for POS tagging and 91.21% F1 for NER.

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Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…→End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFLegacy edge — source detail unavailable
A Survey on Deep Learning for Named Entity Recognition→End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Long Short-Term MemoryLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Speech recognition with deep recurrent neural networksLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Building a Large Annotated Corpus of English: The Penn TreebankLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→On the Properties of Neural Machine Translation: Encoder–Decoder ApproachesLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Learning to Forget: Continual Prediction with LSTMLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Neural Architectures for Named Entity RecognitionLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→On the difficulty of training Recurrent Neural NetworksLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Bidirectional LSTM-CRF Models for Sequence TaggingLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Learning to forget: continual prediction with LSTMLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→A Fast and Accurate Dependency Parser using Neural NetworksLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity RecognitionLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Design challenges and misconceptions in named entity recognitionLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→An Empirical Exploration of Recurrent Network ArchitecturesLegacy edge — source detail unavailable
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled DataLegacy edge — source detail unavailable
Adam: A Method for Stochastic Optimization→Speech recognition with deep recurrent neural networksLegacy edge — source detail unavailable
Adam: A Method for Stochastic Optimization→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
Understanding the difficulty of training deep feedforward neural networks→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
On the difficulty of training Recurrent Neural Networks→Long Short-Term MemoryLegacy edge — source detail unavailable
On the difficulty of training Recurrent Neural Networks→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
Bidirectional LSTM-CRF Models for Sequence Tagging→Long Short-Term MemoryLegacy edge — source detail unavailable
Bidirectional LSTM-CRF Models for Sequence Tagging→A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled DataLegacy edge — source detail unavailable
Bidirectional LSTM-CRF Models for Sequence Tagging→Speech recognition with deep recurrent neural networksLegacy edge — source detail unavailable
Bidirectional LSTM-CRF Models for Sequence Tagging→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity RecognitionLegacy edge — source detail unavailable
Long Short-Term Memory→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
Learning to Forget: Continual Prediction with LSTM→Long Short-Term MemoryLegacy edge — source detail unavailable
Learning to Forget: Continual Prediction with LSTM→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
Speech recognition with deep recurrent neural networks→Long Short-Term MemoryLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→Design challenges and misconceptions in named entity recognitionLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled DataLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Glove: Global Vectors for Word Representation→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
Glove: Global Vectors for Word Representation→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
A Fast and Accurate Dependency Parser using Neural Networks→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
A Fast and Accurate Dependency Parser using Neural Networks→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
A Fast and Accurate Dependency Parser using Neural Networks→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Bidirectional LSTM-CRF Models for Sequence TaggingLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Design challenges and misconceptions in named entity recognitionLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Long Short-Term MemoryLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled DataLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity RecognitionLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled DataLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches→ADADELTA: An Adaptive Learning Rate MethodLegacy edge — source detail unavailable
A Survey on Deep Learning for Named Entity Recognition→Bidirectional LSTM-CRF Models for Sequence TaggingLegacy edge — source detail unavailable
A Survey on Deep Learning for Named Entity Recognition→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
A Survey on Deep Learning for Named Entity Recognition→Neural Architectures for Named Entity RecognitionLegacy edge — source detail unavailable
A Survey on Deep Learning for Named Entity Recognition→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…→Long Short-Term MemoryLegacy edge — source detail unavailable
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…→Speech recognition with deep recurrent neural networksLegacy edge — source detail unavailable
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…→Glove: Global Vectors for Word RepresentationLegacy edge — source detail unavailable
An Empirical Exploration of Recurrent Network Architectures→Building a Large Annotated Corpus of English: The Penn TreebankLegacy edge — source detail unavailable
An Empirical Exploration of Recurrent Network Architectures→On the difficulty of training Recurrent Neural NetworksLegacy edge — source detail unavailable
An Empirical Exploration of Recurrent Network Architectures→Long Short-Term MemoryLegacy edge — source detail unavailable
An Empirical Exploration of Recurrent Network Architectures→Learning long-term dependencies with gradient descent is difficultLegacy edge — source detail unavailable
An Empirical Exploration of Recurrent Network Architectures→Learning to Forget: Continual Prediction with LSTMLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Long Short-Term Memory	1997	101,359
Adam: A Method for Stochastic Optimization	2014	84,698
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…	2015	50,396
Dropout: a simple way to prevent neural networks from overfitting	2014	34,236
Glove: Global Vectors for Word Representation	2014	34,067
Distributed Representations of Words and Phrases and their Compositionality	2013	18,054
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	2001	12,994
Understanding the difficulty of training deep feedforward neural networks	2010	12,673
Speech recognition with deep recurrent neural networks	2013	8,916
Learning long-term dependencies with gradient descent is difficult	1994	8,610
Building a Large Annotated Corpus of English: The Penn Treebank	1993	7,538
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches	2014	6,683
Learning to Forget: Continual Prediction with LSTM	2000	5,538
ADADELTA: An Adaptive Learning Rate Method	2012	5,532
Natural Language Processing (almost) from Scratch	2011	5,174
Neural Architectures for Named Entity Recognition	2016	4,482
Natural Language Processing (almost) from Scratch	2011	3,991
On the difficulty of training Recurrent Neural Networks	2012	3,801
Bidirectional LSTM-CRF Models for Sequence Tagging	2015	3,287
Learning to forget: continual prediction with LSTM	1999	2,427
A Fast and Accurate Dependency Parser using Neural Networks	2014	1,884
Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition	2002	1,583
Design challenges and misconceptions in named entity recognition	2009	1,491
An Empirical Exploration of Recurrent Network Architectures	2015	1,406
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data	2005	1,370

What cites it, inside the corpus

Paper	Year	Cited
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…	2022	3,836
A Survey on Deep Learning for Named Entity Recognition	2020	1,534

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Topics

Natural Language Processing Techniques	Computer Science
Topic Modeling	Computer Science
Speech Recognition and Synthesis	Computer Science

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