Paper: Probabilistic Unification-Based Integration Of Syntactic And Semantic Preferences For Nominal Compounds

ACL ID C90-2071
Title Probabilistic Unification-Based Integration Of Syntactic And Semantic Preferences For Nominal Compounds
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1990
Authors
  • Dekai Wu (University of California at Berkeley, Berkeley CA)

In this paper, we describe a probabilistic framework for unification-based grammars that facilitates'integrating syntactic a~ld se- m~mtic constraints and preferences. We share many of the concerns found in recent work on massively-parallel language inter- pret'ation models, although the proposal re- flects our belief in the value of a higher-level account that is not stated in terms of dis- tributed'computati0n. We also feel that in- adequate learning theories severely limit ex- 'isting massively-parallel language interpre- tation models. A learning theory is not only interesting in its own right, but must un- derlie,any quantitative account of language interpretation, because the complexity of interaction between constraints and prefer- ences makes ad hoc trial-and-error strate- gies for...