A Method for Recognizing Noisy Romanized Japanese Words in Learner English
IEICE - Transactions on Information and Systems
Blog categorization exploiting domain dictionary and dynamically estimated domains of unknown words
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Recognizing noisy romanized Japanese words in learner English
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
The human language project: building a Universal Corpus of the world's languages
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Viterbi training for PCFGs: hardness results and competitiveness of uniform initialization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Adapting self-training for semantic role labeling
ACLstudent '10 Proceedings of the ACL 2010 Student Research Workshop
Morpho Challenge competition 2005--2010: evaluations and results
SIGMORPHON '10 Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology
Graph-based clustering for computational linguistics: a survey
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
Semi-supervised dependency parsing using generalized tri-training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Robust semi-supervised and ensemble-based methods in word sense disambiguation
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
The effect of semi-supervised learning on parsing long distance dependencies in German and Swedish
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
On-line multi-view forests for tracking
Proceedings of the 32nd DAGM conference on Pattern recognition
Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Identification of fine grained feature based event and sentiment phrases from business news stories
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Semisupervised condensed nearest neighbor for part-of-speech tagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning regional transliteration variants
Information Processing and Management: an International Journal
A weakly-supervised approach to argumentative zoning of scientific documents
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised semantic role induction with graph partitioning
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A link-analysis-based discriminant analysis for exploring partially labeled graphs
Pattern Recognition Letters
A document is known by the company it keeps: neighborhood consensus for short text categorization
Language Resources and Evaluation
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The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offers self-contained coverage of semisupervised methods that includes background material on supervised and unsupervised learning. The book presents a brief history of semisupervised learning and its place in the spectrum of learning methods before moving on to discuss well-known natural language processing methods, such as self-training and co-training. It then centers on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, support vector machines (SVMs), and the null-category noise model. In addition, the book covers clustering, the expectation-maximization (EM) algorithm, related generative methods, and agreement methods. It concludes with the graph-based method of label propagation as well as a detailed discussion of spectral methods. Taking an intuitive approach to the material, this lucid book facilitates the application of semisupervised learning methods to natural language processing and provides the framework and motivation for a more systematic study of machine learning.