The Journal of Machine Learning Research is pleased to announce the Special
Issue on Machine Learning Approaches to Shallow Parsing, available online at
http://www.jmlr.org.
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JMLR Special Issue on Shallow Parsing - contents:
Introduction to Special Issue on Machine Learning Approaches to Shallow
Parsing - James Hammerton, Miles Osborne, Susan Armstrong, Walter Daelemans
pp. 551-558
Memory-Based Shallow Parsing - Erik F. Tjong Kim Sang
pp. 559-594
Shallow Parsing using Specialized HMMs - Antonio Molina, Ferran Pla
pp. 595-613
Text Chunking based on a Generalization of Winnow - Tong Zhang, Fred
Damerau, David Johnson
pp. 615-637
Shallow Parsing with PoS Taggers and Linguistic Features - Beata Megyesi
pp. 639-668
Learning Rules and Their Exceptions - Herve Dejean
pp. 669-693
Shallow Parsing using Noisy and Non-Stationary Training Material - Miles
Osborne
pp. 695-719
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All papers in the special issue, as well as all previous JMLR papers, are
available electronically at http://www.jmlr.org/ in PostScript and PDF
formats. Many are also available in HTML. The papers of Volume 1 are also
available in hardcopy from the MIT Press; please see
http://mitpress.mit.edu/JMLR for details.
-David Cohn, <David.Cohn@acm.org>
Managing Editor, Journal of Machine Learning Research
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