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

  title={Improvements in Part-of-Speech Tagging with an Application to German},
  author={Helmut Schmid},
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 
Albanian Part-of-Speech Tagging: Gold Standard and Evaluation
This paper provides mappings from the full tagset to both the original Google Universal Part-of-Speech Tags and the variant used in the Universal Dependencies project and achieves accuracies of up to 95.10%.
Part-of-Speech Tagging and Partial Parsing
The initial impetus for the current popularity of statistical methods in computational linguistics was provided in large part by the papers on part-of-speech tagging by Church, DeRose, and Garside, which showed that it was indeed possible to carve part- of-speech disambiguation out of the apparently monolithic problem of natural language understanding, and solve it with impressive accuracy.
Domain Adaptation in Part-of-Speech Tagging
The goal of this chapter is to highlight solutions that handle labeled and unlabeled data, methods that deal with such data to solve the domain adaptation problem, and to present a case study that has achieved significant accuracy rates on tagging journalistic and scientific texts.
EVALUATING PART-OF-SPEECH TAGGING AND PARSING On the Evaluation of Automatic Parsing of Natural Language
This chapter introduces the reader to the evaluation of part-of- speech taggers and parsers and raises a point about the issue of input data segmentation into linguistic units, a crucial step in any evaluation related to language processing.
Automatic correction of part-of-speech corpora
In this study a simple method for automatic correction of part-ofspeech corpora is presented, which works as follows: Initially two or more already available part-of-speech taggers are applied on the
Is Part-of-Speech Tagging a Solved Task? An Evaluation of POS Taggers for the German Web as Corpus
It is found that HMM taggers are more robust and much faster than advanced machine-learning approaches such as MaxEnt and promising directions for future research are unsupervised learning of a tagger lexicon from large unannotated corpora, as well as developing adaptive tagging models.
Spectral Graph Convolutional Networks for Part-of-Speech Tagging
The novelty proposed in this thesis is to translate a corpus into a graph as a direct input for the GCN, a recently developed neural network model, called graph convolutional network (GCN).
Improving Data Driven Part-of-Speech Tagging by Morphologic Knowledge Induction
A Markov part-of-speech tagger for which the P (w|t) emission probabilities of word w given tag t are replaced by a linear interpolation of tag emission probabilities given a list of representations of w, allowing the derivation of linguistically meaningful string suffixes that may relate to certain POS labels.
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
Extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.
Exploiting languages proximity for part-of-speech tagging of three French regional languages
This paper addresses the case when no labeled data in the targeted language and no parallel corpus are available, and proposes different strategies to combine them and improve the state-of-the-art of part- of-speech tagging in this difficult scenario.


A Practical Part-of-Speech Tagger
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
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
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
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
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
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
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.