Abstract
A model of concept acquisition is proposed, which combines various forms of linguistic and conceptual evidence that arise in the course of text understanding processes. We use terminological classification for the creation and management of concept hypotheses, for their incremental annotation by assertions which reflect the quality of available evidence, and for their subsequent evaluation and selection.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
D. Aha, D. Kibler, and M. Albert. Instance-based learning algorithms. Machine Learning, 6:37–66, 1991.
N. Bröker, S. Schacht, P. Neuhaus, and U. Hahn. Performanzorientiertes Parsing und Grammatik-Design: das ParseTalk-System. In C. Habel, S. Kanngießer, and G. Rickheit, editors, Perspektiven der Kognitiven Linguistik. Modelle und Methoden, pages 79–125. Opladen: Westdeutscher Verlag, 1996.
F. Gomez and C. Segami. The recognition and classification of concepts in understanding scientific texts. Journal of Experimental and Theoretical Artificial Intelligence, 1:51–77, 1989.
U. Hahn, M. Klenner, and K. Schnattinger. Learning from texts: a terminological metareasoning perspective. In S. Wermter, E. Riloff, and G. Scheler, editors, Connectionist, Statistical and Symbolic Approaches to Learning in Natural Language Processing, pages 453–468. Berlin: Springer, 1996.
U. Hahn, K. Schnattinger, and M. Romacker. Automatic knowledge acquisition from medical texts. In AMIA'96 — Proc. AMIA Fall Symposium Beyond the Superhighway: Exploiting the Internet with Medical Informatics, pages 383–387, 1996.
P. Hastings, Implications of an automatic lexical acquisition system. In S. Wermter, E. Riloff, and G. Scheler, editors, Connectionist, Statistical and Symbolic Approaches to Learning in Natural Language Processing, pages 261–274. Berlin: Springer, 1996.
R. Mooney, Integrated learning of words and their underlying concepts. In CogSci'87 — Proc. 9th Conf. of the Cognitive Science Society, pages 974–978, 1987.
K. Moorman and A. Ram. The role of ontology in creative understanding. In CogSci'96 — Proc. 18th Conf. of the Cognitive Science Society, pages 98–103, 1996.
B. Nebel, Berlin: Springer, 1990.
L. Rau, P. Jacobs, and U. Zernik. Information extraction and text summarization using linguistic knowledge acquisition. Information Processing & Management, 25(4):419–428, 1989.
E. Riloff. Automatically constructing a dictionary for information extraction tasks. In AAAI'93 — Proc. 11th National Conf. on Artificial Intelligence, pages 811–816, 1993.
K. Schnattinger and U. Hahn. A terminological qualification calculus for preferential reasoning under uncertainty. In KI'96–Proc. 20th Annual German Conf. on Artificial Intelligence, pages 349–362. Berlin: Springer, 1996.
K. Schnattinger, U. Hahn, and M. Klenner. Quality-based terminological reasoning for concept learning. In KI'95-Proc. 19th Annual German Conf, on Artificial Intelligence, pages 113–124. Berlin: Springer, 1995.
S. Soderland, D. Fisher, J. Aseltine, and W. Lehnert, CRYSTAL: Inducing a conceptual dictionary. In IJCAI'95 — Proc. 14th Intl. Joint Conf. on Artificial Intelligence, pages 1314–1319, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schnattinger, K., Hahn, U. (1997). Constraining the acquisition of concepts by the quality of heterogeneous evidence. In: Brewka, G., Habel, C., Nebel, B. (eds) KI-97: Advances in Artificial Intelligence. KI 1997. Lecture Notes in Computer Science, vol 1303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540634932_20
Download citation
DOI: https://doi.org/10.1007/3540634932_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63493-5
Online ISBN: 978-3-540-69582-0
eBook Packages: Springer Book Archive