Tagging English text with a probabilistic model
Computational Linguistics
Parameter estimation for probabilistic finite-state transducers
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
An interactive spreadsheet for teaching the forward-backward algorithm
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Training conditional random fields using incomplete annotations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Multi-criteria-based strategy to stop active learning for data annotation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Learning and inference with constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Disambiguation of preposition sense using linguistically motivated features
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
SemEval-2007 task 06: word-sense disambiguation of prepositions
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Minimized models for unsupervised part-of-speech tagging
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Assessing the benefits of partial automatic pre-labeling for frame-semantic annotation
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
Efficient optimization of an MDL-inspired objective function for unsupervised part-of-speech tagging
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
A semi-supervised word alignment algorithm with partial manual alignments
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
What's in a preposition?: dimensions of sense disambiguation for an interesting word class
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Models and training for unsupervised preposition sense disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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For many NLP tasks, EM-trained HMMs are the common models. However, in order to escape local maxima and find the best model, we need to start with a good initial model. Researchers suggested repeated random restarts or constraints that guide the model evolution. Neither approach is ideal. Restarts are time-intensive, and most constraint-based approaches require serious re-engineering or external solvers. In this paper we measure the effectiveness of very limited initial constraints: specifically, annotations of a small number of words in the training data. We vary the amount and distribution of initial partial annotations, and compare the results to unsupervised and supervised approaches. We find that partial annotations improve accuracy and can reduce the need for random restarts, which speeds up training time considerably.