Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
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Summary
Experimental results on part-of-speech tagging and base noun phrase chunking are given, in both cases showing improvements over results for a maximum-entropy tagger.
- Type
- article
- Published
- 2002-07-06
- Cited by
- 2,303
- References
- 15
- Access
- Open access
- OpenAlex
- https://openalex.org/W2008652694
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10888973
Keywords
Maximum-entropy Markov model, Computer science, Discriminative model, Hidden Markov model, Conditional random field
References
- Building a Large Annotated Corpus of English: The Penn Treebank
- A Maximum Entropy Model for Part-Of-Speech Tagging
- Maximum Entropy Markov Models for Information Extraction and Segmentation
- On Weak Learning
- Large Margin Classification Using the Perceptron Algorithm
- Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger
- The perceptron: a probabilistic model for information storage and organization in the brain.
- Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging
- New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron
- Ranking Algorithms for Named Entity Extraction: Boosting and the VotedPerceptron
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
- Text Chunking using Transformation-Based Learning
- Convolution Kernels for Natural Language
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- Global vs. Local: Component Based Learning for Classification and Image Understanding
- Counting-MLNs: Learning Relational Structure for Decision Making
- Loss Functions for Discriminative Training of Energy-Based Models
- Tree Revision Learning for Dependency Parsing
- Structured Sparsity in Structured Prediction
- Semi-Markov Models for Sequence Segmentation
- Online Learning of Structured Predictors with Multiple Kernels
- A universal multilingual weightless neural network tagger via quantitative linguistics
- Ensemble Methods for Structured Prediction
- Large Margin Boltzmann Machines
- Robust Information Extraction with Perceptrons
- Learning to Rank Documents for Ad-Hoc Retrieval with Regularized Models
- Universal Schema for Knowledge Representation from Text and Structured Data
- Intégration de l’alignement de mots dans le concordancier bilingue TransSearch
- Microsoft Research Treelet Translation System: NIST MT Evaluation 06
- Learning via Inference over Structurally Constrained Output
- A Resource and Tool for Super-sense Tagging of Italian Texts
- A Multilingual Dependency Analysis System Using Online Passive-Aggressive Learning
- Towards a machine-learning architecture for lexical functional grammar parsing
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