An Empirical Approach to Conceptual Case Frame Acquisition. Riloff, E. and Schmelzenbach, M. In Proceedings of the Sixth Workshop on Very Large Corpora, pages 49-56, 1998. In Proceedings of the Sixth Workshop on Very Large Corpora.
An Empirical Approach to Conceptual Case Frame Acquisition [link]Website  abstract   bibtex   
Conceptual natural language processing systems usually rely on case frame instantiation to-recognize events and role objects in text. But generating a good set of case frames for a domain is timeconsuming, tedious, and prone to errors of omission. We have developed a corpus-based algorithm for acquiring conceptual case frames empirically from unannotated text. Our algorithm builds on previous research on corpus-based methods for acquiring extraction patterns and semantic lexicons. Giv. en extraction patterns and a semantic lexicon for a domain, our algorithm learns semantic preferences for each extraction pattern and merges the syntactically compatible patterns to produce multi-slot case frames with selectional restrictions. The case frames generate more cohesive output and produce fewer false hits than the original extraction patterns. Our system requires only proclassified training texts and a few hours of manual review to filter the dictionar- ies, demonstrating that conceptual case frames can be acquired from unannotated text without special training resources.
@inProceedings{
 title = {An Empirical Approach to Conceptual Case Frame Acquisition},
 type = {inProceedings},
 year = {1998},
 pages = {49-56},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.2378},
 publisher = {In Proceedings of the Sixth Workshop on Very Large Corpora},
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 abstract = {Conceptual natural language processing systems usually rely on case frame instantiation to-recognize events and role objects in text. But generating a good set of case frames for a domain is timeconsuming, tedious, and prone to errors of omission. We have developed a corpus-based algorithm for acquiring conceptual case frames empirically from unannotated text. Our algorithm builds on previous research on corpus-based methods for acquiring extraction patterns and semantic lexicons. Giv. en extraction patterns and a semantic lexicon for a domain, our algorithm learns semantic preferences for each extraction pattern and merges the syntactically compatible patterns to produce multi-slot case frames with selectional restrictions. The case frames generate more cohesive output and produce fewer false hits than the original extraction patterns. Our system requires only proclassified training texts and a few hours of manual review to filter the dictionar- ies, demonstrating that conceptual case frames can be acquired from unannotated text without special training resources.},
 bibtype = {inProceedings},
 author = {Riloff, Ellen and Schmelzenbach, Mark},
 booktitle = {Proceedings of the Sixth Workshop on Very Large Corpora}
}
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