Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM

@inproceedings{Darling2012UnsupervisedPT,
  title={Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM},
  author={William M. Darling and Michael J. Paul and Fei Song},
  year={2012}
}
Unsupervised part-of-speech (POS) tagging has recently been shown to greatly benefit from Bayesian approaches where HMM parameters are integrated out, leading to significant increases in tagging accuracy. These improvements in unsupervised methods are important especially in specialized social media domains such as Twitter where little training data is available. Here, we take the Bayesian approach one step further by integrating semantic information from an LDA-like topic model with an HMM… CONTINUE READING
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