Special semi-supervised techniques for natural language processing tasks

Authors:
Richárd Farkas;György Szarvas;János Csirik
Affiliations:
University of Szeged, Department of Informatics, Szeged, Hungary;University of Szeged, Department of Informatics, Szeged, Hungary;Hungarian Academy of Sciences, Research Group on Artificial Intelligence, Szeged, Hungary
Venue:
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2007

Citing 6
Cited 0

Foundations of statistical natural language processing

Foundations of statistical natural language processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition

CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Unsupervised named-entity extraction from the Web: An experimental study

Artificial Intelligence
Improving a state-of-the-art named entity recognition system using the world wide web

ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
A multilingual named entity recognition system using boosting and c4.5 decision tree learning algorithms

DS'06 Proceedings of the 9th international conference on Discovery Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

A labeled natural language corpus is often difficult, expensive or time-consuming to obtain as its construction requires expert human effort. On the other hand, unlabelled texts are available in abundance thanks to the World Wide Web. The importance of utilizing unlabeled data in machine learning systems is growing. Here, we investigate classic semi-supervised approaches and examine the potential advantages of applying special techniques for Natural Language Processing tasks.