Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
Language analysis in not-so-limited domains
ACM '86 Proceedings of 1986 ACM Fall joint computer conference
The organization of knowledge in a multi-lingual, integrated parser (natural language, translation)
The organization of knowledge in a multi-lingual, integrated parser (natural language, translation)
A knowledge-based approach to language production (natural, generation)
A knowledge-based approach to language production (natural, generation)
TINLAP '75 Proceedings of the 1975 workshop on Theoretical issues in natural language processing
Semantic interpretation using KL-ONE
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Semantics-first natural language processing
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
Recent research in language analysis and language generation has highlighted the role of knowledge representation in both processes. Certain knowledge representation foundations, such as structured inheritance networks and feature-based linguistic representations, have proved useful in a variety of language processing tasks. Augmentations to this common framework, however, are required to handle particular issues, such as the ROLE RELATIONSHIP problem: the task of determining how roles, or slots, of a given frame, are filled based on knowledge about other roles. Three knowledge structures are discussed that address this problem. The semantic interpreter of an analyzer called TRUMP (TRansportable Understanding Mechanism Package) uses these structures to determine the fillers of roles effectively without requiring excessive specialized information about each frame.