Automatic induction of rules for text simplification


Long and complicated sentences pose various problems to many state-of-the-art natural language technologies. We have been exploring methods to automatically transform such sentences as to make them simpler. These methods involve the use of a rule-based system, driven by the syntax of the text in the domain of interest. Hand-crafting rules for every domain is time-consuming and impractical. This paper describes an algorithm and an implementation by which generalized rules for simplification are automatically induced from annotated training material with a novel partial parsing technique which combines constituent structure and dependency information. This algorithm described in the paper employs example-based generalizations on linguistically-motivated structures. Disciplines Cognitive Neuroscience | Theory and Algorithms Comments University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-96-30. This technical report is available at ScholarlyCommons:

DOI: 10.1016/S0950-7051(97)00029-4

Extracted Key Phrases

Citations per Year

149 Citations

Semantic Scholar estimates that this publication has 149 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Chandrasekar1997AutomaticIO, title={Automatic induction of rules for text simplification}, author={Raman Chandrasekar and Srinivas Bangalore}, journal={Knowl.-Based Syst.}, year={1997}, volume={10}, pages={183-190} }