Show simple item record

Aspectual Modifications to a LCS Database for NLP Applications

dc.contributor.authorDorr, Bonnie J.en_US
dc.contributor.authorOlsen, Mari Bromanen_US
dc.date.accessioned2004-05-31T22:45:14Z
dc.date.available2004-05-31T22:45:14Z
dc.date.created1997-05en_US
dc.date.issued1998-10-15en_US
dc.identifier.urihttp://hdl.handle.net/1903/888
dc.description.abstractVerbal and compositional lexical aspect provide the underlying temporal structure of events. Knowledge of lexical aspect, e.g., (a)telicity, is therefore required for interpreting event sequences in discourse (Dowty, 1986: Moens and Steedman, 1988; Passoneau, 1988), interfacing to temporal databases (Androutsopoulos, 1996), processing temporal modifiers (Antonisse, 1994), describing allowable alternations and their semantic effects (Resnik, 1996; Tenny, 1994), and selecting tense and lexical items for natural language generation ((Dorr and Olsen, 1996; Klavans and Chodorow, 1992), cf. (Slobin and Bocaz, 1988)). We show that it is possible to represent lexical aspect---both verbal and compositional---on a large scale, using Lexical Conceptual Structure (LCS) representations of verbs in the classes cataloged by Levin (1993). We show how proper consideration of these universal pieces of verb meaning may be used to refine lexical representations and derive a range of meanings from combinations of LCS representations. A single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentence levels. Finally, we illustrate how knowledge of lexical aspect facilitates the interpretation of events in NLP applications. (Also cross-referenced as UMIACS-TR-97-21) (Also cross-referenced as LAMP-TR-007)en_US
dc.format.extent167190 bytes
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3763en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-97-21en_US
dc.relation.ispartofseriesLAMP-TR-007en_US
dc.titleAspectual Modifications to a LCS Database for NLP Applicationsen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record