Shallow parsing with conditional random fields

Authors:
Fei Sha;Fernando Pereira
Affiliations:
University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA
Venue:
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Year:
2003

Citing 18
Cited 293

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ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Self-supervised capturing of users' activities from weblogs

International Journal of Intelligent Information and Database Systems
Tree-structured conditional random fields for semantic annotation

ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Applying conditional random fields to chinese shallow parsing

CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Sentence role identification in medline abstracts: training classifier with structured abstracts

AM'03 Proceedings of the Second international conference on Active Mining
Named entity recognition in tweets: an experimental study

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Chunking using conditional random fields in korean texts

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Tense tagging for verbs in cross-lingual context: a case study

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Regularisation techniques for conditional random fields: parameterised versus parameter-free

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Segmentation conditional random fields (SCRFs): a new approach for protein fold recognition

RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Exploiting inference for approximate parameter learning in discriminative fields: an empirical study

EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A Generic Approach for Systematic Analysis of Sports Videos

ACM Transactions on Intelligent Systems and Technology (TIST)
Syntactic chunking across different corpora

MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Stochastic web-based natural language dialog planning for web information filtering

Expert Systems with Applications: An International Journal
Comparison of feature-level learning methods for mining online consumer reviews

Expert Systems with Applications: An International Journal
The echo state conditional random field model for sequential data modeling

Expert Systems with Applications: An International Journal
Rich set of features for proper name recognition in polish texts

SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Variational conditional random fields for online speaker detection and tracking

Speech Communication
Empirical comparisons of various discriminative language models for speech recognition

ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
Evolutionary Shallow Natural Language Parsing

Computational Intelligence
Optimizing CRF-Based model for proper name recognition in polish texts

CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Developing an algorithm for mining semantics in texts

CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Generative structure learning for Markov logic networks based on graph of predicates

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A multiobjective simulated annealing approach for classifier ensemble: Named entity recognition in Indian languages as case studies

Expert Systems with Applications: An International Journal
P-top-k queries in a probabilistic framework from information extraction models

Computers & Mathematics with Applications
Neural networks letter: Training the max-margin sequence model with the relaxed slack variables

Neural Networks
PROBABILISTIC MODELS FOR FOCUSED WEB CRAWLING

Computational Intelligence
Active learning technique for biomedical named entity extraction

Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Relational Transformation-based Tagging for Activity Recognition

Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Minimum-risk training of approximate CRF-based NLP systems

NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Iterative viterbi A* algorithm for k-best sequential decoding

ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Hierarchical chunk-to-string translation

ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
A cost sensitive part-of-speech tagging: differentiating serious errors from minor errors

ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Exploiting chunk-level features to improve phrase chunking

EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A novel discriminative framework for sentence-level discourse analysis

EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A preliminary work on symptom name recognition from free-text clinical records of traditional chinese medicine using conditional random fields and reasonable features

BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Blinking-based live face detection using conditional random fields

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Transliteration by sequence labeling with lattice encodings and reranking

NEWS '12 Proceedings of the 4th Named Entity Workshop
Interpreting keyword queries over web knowledge bases

Proceedings of the 21st ACM international conference on Information and knowledge management
Labeling TV stream segments with conditional random fields

MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Segmentation and classification of objects with implicit scene context

Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Finite-state chart constraints for reduced complexity context-free parsing pipelines

Computational Linguistics
A conditional random field-based model for joint sequence segmentation and classification

Pattern Recognition
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Data & Knowledge Engineering
Extraction and Compilation of Events and Sub-events from Twitter

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Full Length Article: Simulated annealing based classifier ensemble techniques: Application to part of speech tagging

Information Fusion
Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information

ACM Transactions on Asian Language Information Processing (TALIP)
RNA secondary structure prediction using conditional random fields model

International Journal of Data Mining and Bioinformatics
Semi-supervised constituent grammar induction based on text chunking information

CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Turkish constituent chunking with morphological and contextual features

CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Capturing programming content in online discussions

Proceedings of the seventh international conference on Knowledge capture
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ACM Transactions on Asian Language Information Processing (TALIP)
CRF framework for supervised preference aggregation

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Semi-supervised learning of hidden conditional random fields for time-series classification

Neurocomputing
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SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Complex Terminology Extraction Model from Unstructured Web Text Based Linguistic and Statistical Knowledge

International Journal of Information Retrieval Research
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Proceedings of the VLDB Endowment
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ACM Transactions on Speech and Language Processing (TSLP)
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Theoretical Computer Science
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Pattern Recognition
3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning

Machine Vision and Applications
Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: An empirical study

Journal of Biomedical Informatics

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Abstract

Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.