Discriminative training methods for hidden Markov models

Michael Collins

doi:https://doi.org/10.3115/1118693.1118694

Discriminative training methods for hidden Markov models

2002 · 1,895 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.

9 of 9 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.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

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

Understanding Machine Learning: From Theory To Algorithms→Discriminative training methods for hidden Markov modelsLegacy 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
Markov logic networks→Discriminative training methods for hidden Markov modelsLegacy edge — source detail unavailable
Large Margin Methods for Structured and Interdependent Output Variables→Discriminative training methods for hidden Markov modelsLegacy edge — source detail unavailable
Design challenges and misconceptions in named entity recognition→Discriminative training methods for hidden Markov modelsLegacy 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
Discriminative training methods for hidden Markov models→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
Discriminative training methods for hidden Markov models→Building a Large Annotated Corpus of English: The Penn TreebankLegacy edge — source detail unavailable
Discriminative training methods for hidden Markov models→Transformation-based error-driven learning and natural language processing: a case study …Legacy edge — source detail unavailable
Feature-rich part-of-speech tagging with a cyclic dependency network→Building a Large Annotated Corpus of English: The Penn TreebankLegacy edge — source detail unavailable
Feature-rich part-of-speech tagging with a cyclic dependency network→Transformation-based error-driven learning and natural language processing: a case study …Legacy 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
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→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Large Margin Methods for Structured and Interdependent Output Variables→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable
Transformation-based error-driven learning and natural language processing: a case study …→Building a Large Annotated Corpus of English: The Penn TreebankLegacy edge — source detail unavailable
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data→Transformation-based error-driven learning and natural language processing: a case study …Legacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→The perceptron: A probabilistic model for information storage and organization in the bra…Legacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence DataLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	2001	12,994
The perceptron: A probabilistic model for information storage and organization in the bra…	1958	11,965
Building a Large Annotated Corpus of English: The Penn Treebank	1993	7,538
Transformation-based error-driven learning and natural language processing: a case study …	1995	1,535

What cites it, inside the corpus

Paper	Year	Cited
Understanding Machine Learning: From Theory To Algorithms	2015	3,081
Feature-rich part-of-speech tagging with a cyclic dependency network	2003	2,885
Markov logic networks	2006	2,684
Large Margin Methods for Structured and Interdependent Output Variables	2005	1,957
Design challenges and misconceptions in named entity recognition	2009	1,491

Links

Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record

Topics

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

Is this record sound?

complete

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

supports1 author record(s) attached.
supports12 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

sha256 930f5bc64fea8a60…