and weighted voting. This paper is the first publication to present experiments on data-driven part-of-speech tagging for Kiswahili and Bantu languages in general. 1

We investigate the accuracy of alternative part-of-speech tag models and their impact on parser performance. In addition to considering single-tag and multipletag per word input, tag selection models which draw on information available from the parser are applied. Results indicate that given a

We use a Dynamic Bayesian Network (dbn) to represent compactly a variety of sublexical and contextual features relevant to Part-of-Speech (PoS) tagging. The outcome is a flexible tagger (LegoTag) with state-of-the-art performance (3.6 % error on a benchmark corpus). We explore the effect

algorithm using antagonistic adversaries and compare it to previous proposals on 12 multilingual cross-domain part-of-speech tagging datasets. While previous approaches do not improve on our supervised baseline, our approach is better across the board with an average 4 % error reduction. 1

Abstract — We demonstrate that a simple hidden Markov model can achieve state of the art performance in unsupervised part-of-speech tagging, by improving aspects of standard Baum-Welch (EM) estimation. One improvement uses word similarities to smooth the lexical tag ¢ word probability estimates

We investigate the use of selective classi-fiers for part-of-speech tagging (POS). The idea is to allow classifiers to abstain on hard instances, passing them to down-stream classifiers that may have more context available. In this report we focus on just the first stage of such a cascade, and ask

This paper presents a couple of extensions to a basic Markov Model tagger (called TreeTagger) which improve its accuracy when trained on small corpora. The basic tagger was originally developed for English [Schmid, 1994]. The extensions together reduced error rates on a German test corpus by more than a third.

This is an erratum for the paper titled “Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging ” by Täckström et al. (2013). It revises the results with the coupled constraints presented in the last three columns of Table 2 in the aforementioned paper.

We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90 % accuracy. The data and tools have been made available to the research community

m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face