Abstract
Over the past 10-15 years, the influence of methods from machine learning has transformed the way that research is done in the field of natural language processing. This talk will begin by covering the history of this transformation. In particular, learning methods have proved successful in producing stand-alone text-processing components to handle a number of linguistic tasks. Moreover, these components can be combined to produce systems that exhibit shallow text-understanding capabilities: they can, for example, extract key facts from unrestricted documents in limited domains or find answers to general-purpose questions from open-domain document collections. I will briefly describe the state of the art for these practical text-processing applications, focusing on the important role that machine learning methods have played in their development.
The second part of the talk will explore the role that natural language processing might play in machine learning research. Here, I will explain the kinds of text-based features that are relatively easy to incorporate into machine learning data sets. In addition, I’ll outline some problems from natural language processing that require, or could at least benefit from, new machine learning algorithms.
Chapter PDF
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cardie, C. (2005). Machine Learning for Natural Language Processing (and Vice Versa?). In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_2
Download citation
DOI: https://doi.org/10.1007/11564126_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29244-9
Online ISBN: 978-3-540-31665-7
eBook Packages: Computer ScienceComputer Science (R0)