Design challenges and misconceptions in named entity recognition

Lev Ratinov; Dan Roth

doi:https://doi.org/10.3115/1596374.1596399

Design challenges and misconceptions in named entity recognition

2009 · 1,491 citations · 5 from inside this corpus

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

14 of 14 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Natural Language Processing (almost) from Scratch→Design challenges and misconceptions in named entity recognitionLegacy edge — source detail unavailable
Neural Architectures for Named Entity Recognition→Design challenges and misconceptions in 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
Word Representations: A Simple and General Method for Semi-Supervised Learning→Design challenges and misconceptions in named entity recognitionLegacy edge — source detail unavailable
Knowledge Graphs→Design challenges and misconceptions in named entity recognitionLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→A tutorial on hidden Markov models and selected applications in speech recognitionLegacy 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→Incorporating non-local information into information extraction systems by Gibbs samplingLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Class-based n -gram models of natural languageLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Feature-rich part-of-speech tagging with a cyclic dependency networkLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Discriminative training methods for hidden Markov modelsLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→A Tutorial on Hidden Markov Models and Selected Applications in Speech RecognitionLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Predicting good probabilities with supervised learningLegacy 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
Feature-rich part-of-speech tagging with a cyclic dependency network→Discriminative training methods for hidden Markov modelsLegacy edge — source detail unavailable
Feature-rich part-of-speech tagging with a cyclic dependency network→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Discriminative training methods for hidden Markov models→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Incorporating non-local information into information extraction systems by Gibbs sampling→A tutorial on hidden Markov models and selected applications in speech recognitionLegacy edge — source detail unavailable
Incorporating non-local information into information extraction systems by Gibbs sampling→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Incorporating non-local information into information extraction systems by Gibbs sampling→A Tutorial on Hidden Markov Models and Selected Applications in Speech RecognitionLegacy edge — source detail unavailable
Word Representations: A Simple and General Method for Semi-Supervised Learning→Class-based n -gram models of natural languageLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→Feature-rich part-of-speech tagging with a cyclic dependency networkLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→Class-based n -gram models of natural languageLegacy edge — source detail unavailable
Natural Language Processing (almost) from Scratch→A tutorial on hidden Markov models and selected applications in speech recognitionLegacy 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
Natural Language Processing (almost) from Scratch→Word Representations: A Simple and General Method for Semi-Supervised LearningLegacy 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→Word Representations: A Simple and General Method for Semi-Supervised LearningLegacy 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
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→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→Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity RecognitionLegacy edge — source detail unavailable
Knowledge Graphs→Incorporating non-local information into information extraction systems by Gibbs samplingLegacy edge — source detail unavailable
Knowledge Graphs→Neural Architectures for Named Entity RecognitionLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
A tutorial on hidden Markov models and selected applications in speech recognition	1989	22,910
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	2001	12,994
Incorporating non-local information into information extraction systems by Gibbs sampling	2005	3,044
Class-based n -gram models of natural language	1992	2,894
Feature-rich part-of-speech tagging with a cyclic dependency network	2003	2,885
Discriminative training methods for hidden Markov models	2002	1,895
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition	1990	1,889
Predicting good probabilities with supervised learning	2005	1,751
Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition	2002	1,583

What cites it, inside the corpus

Paper	Year	Cited
Natural Language Processing (almost) from Scratch	2011	5,174
Neural Architectures for Named Entity Recognition	2016	4,482
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF	2016	2,595
Word Representations: A Simple and General Method for Semi-Supervised Learning	2010	1,941
Knowledge Graphs	2021	1,767

Links

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Topics

Topic Modeling	Computer Science
Natural Language Processing Techniques	Computer Science
Data Quality and Management	Decision Sciences

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complete

Nothing in this record contradicts itself and no field we check is missing.

supports2 author record(s) attached.
supports36 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

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