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A Maximum Entropy Model for Part-Of-Speech Tagging

by Adwait Ratnaparkhi , 1996
"... 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 "features" t ..."
Abstract - Cited by 580 (1 self) - Add to MetaCart
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 "

Probabilistic Part-of-Speech Tagging Using Decision Trees

by Helmut Schmid , 1994
"... 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, ..."
Abstract - Cited by 1058 (9 self) - Add to MetaCart
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

Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network

by Kristina Toutanova , Dan Klein, Christopher D. Manning, Yoram Singer - IN PROCEEDINGS OF HLT-NAACL , 2003
"... 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) effective ..."
Abstract - Cited by 693 (23 self) - Add to MetaCart
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

Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging

by Eric Brill - Computational Linguistics , 1995
"... this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
Abstract - Cited by 924 (8 self) - Add to MetaCart
of this learning method applied to part of speech tagging

Equations for Part-of-Speech Tagging

by Eugene Charniak, Curtis Hendrickson, Neil Jacobson, Mike Perkowitz - In Proceedings of the Eleventh National Conference on Artificial Intelligence , 1993
"... We derive from first principles the basic equations for a few of the basic hidden-Markov-model word taggers as well as equations for other models which may be novel (the descriptions in previous papers being too spare to be sure). We give performance results for all of the models. The results from o ..."
Abstract - Cited by 129 (2 self) - Add to MetaCart
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

Analysis of Part of Speech Tagging

by P. S. Patheja, Akhilesh A. Waoo, Richa Garg
"... 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 of the prepro ..."
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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 part of speech tagging

by Emad Mohamed - In Proceedings of LREC, Valetta, Malta. Antal van den Bosch, Erwin Marsi, and Abdelhadi Soudi , 2010
"... 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 uses ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
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

Improvements In Part-of-Speech Tagging With an Application To German

by Helmut Schmid - In Proceedings of the ACL SIGDAT-Workshop , 1995
"... 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 th ..."
Abstract - Cited by 216 (1 self) - Add to MetaCart
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.

Distributional Part-of-Speech Tagging

by Hinrich Schütze - In Proc. of 7th Conference of the European Chapter of the Association for Computational Linguistics , 1995
"... 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. ..."
Abstract - Cited by 114 (8 self) - Add to MetaCart
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.

Some advances in transformation-based part-of-speech tagging

by Eric Brill - In Proceedings of the Twelfth National Conference on Artificial Intelligence , 1994
"... 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 wa ..."
Abstract - Cited by 294 (1 self) - Add to MetaCart
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
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