This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "

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 this tagging method, transition probabilities are estimated using a decision tree. Based on this method

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii

of this learning method applied to part of speech tagging

the equations for a variety of models may be derived and thus encourage future authors to give the equations for their model and the derivations thereof. Introduction The last few years have seen a fair number of papers on part-of-speech tagging --- assigning the correct part of speech to each word in a text

In the area of text mining, Natural Language Processing is an emerging field. As text is an unstructured source of information, to make it a suitable input to an automatic method of information extraction it is usually transformed into a structured format. Part of Speech Tagging is one

Arabic is a morphologically rich language, which presents a challenge for part of speech tagging. In this paper, we compare two novel methods for POS tagging of Arabic without the use of gold standard word segmentation but with the full POS tagset of the Penn Arabic Treebank. The first approach

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 paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.

Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In (Brill 1992), a trainable rule-based tagger