2007
NIPS
NeurIPS 2007
A Bayesian LDA-based model for semi-supervised part-of-speech tagging
Abstract
We present a novel Bayesian model for semi-supervised part-of-speech tagging. Our model extends the Latent Dirichlet Allocation model and incorporates the intuition that wordsโ distributions over tags, p(t|w), are sparse. In addition we in- troduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outper- forms the best previously proposed model for this task on a standard dataset.
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Topic Pioneer
- Part-of-Speech Tagging
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Interdisciplinary Bridge
- Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
- part-of-speech tagging
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Hot Topic Early Bird
- semi-supervised learning
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Trend Setter
- Part-of-Speech Tagging
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Natural Language Processing > Understanding > Part-of-Speech Tagging
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling