Improvements in Part-of-Speech Tagging with an Application to German

@inproceedings{Schmid1999ImprovementsIP,
  title={Improvements in Part-of-Speech Tagging with an Application to German},
  author={Helmut Schmid},
  year={1999}
}
Work on part-of-speech tagging has concentrated on English in the past, since a lot of manually tagged training material is available for English and results can be compared to those of other researchers. It was assumed that methods which have been developed for English would work for other languages as well.1 
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References

SHOWING 1-10 OF 16 REFERENCES
A Practical Part-of-Speech Tagger
TLDR
An implementation of a part-of-speech tagger based on a hidden Markov model that enables robust and accurate tagging with few resource requirements and accuracy exceeds 96%.
Unsupervised Learning of Disambiguation Rules for Part of Speech Tagging
TLDR
An unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus is described and compared to the Baum-Welch algorithm, used for unsuper supervised training of stochastic taggers.
Probabilistic part-of-speech tagging using decision trees
In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In
Tagging English Text with a Probabilistic Model
TLDR
Experminents show that the best training is obtained by using as much tagged text as possible, and show that Maximum Likelihood training, the procedure that is routinely used to estimate hidden Markov models parameters from training data, will not necessarily improve the tagging accuracy.
Implementation and evaluation of a German HMM for POS disambiguation
A German language model for the Xerox HMM tagger is presented. This model’s performance is compared with two other German taggers with partial parameter re-estimation and full adaption of parameters
A Simple Rule-Based Part of Speech Tagger
TLDR
This work presents a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers, demonstrating that the stochastics method is not the only viable method for part ofspeech tagging.
Building a Large Annotated Corpus of English: The Penn Treebank
TLDR
As a result of this grant, the researchers have now published on CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, which includes a fully hand-parsed version of the classic Brown corpus.
Estimation of probabilities from sparse data for the language model component of a speech recognizer
  • S. Katz
  • Computer Science
    IEEE Trans. Acoust. Speech Signal Process.
  • 1987
TLDR
The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data, and compares favorably to other proposed methods.
Beyond Word N-Grams
TLDR
The low perplexity achieved by relatively small PST mixture models suggests that they may be an advantageous alternative, both theoretically and practically, to the widely used n-gram models.
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