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Logical form generation as abduction: Part II. A dual‐route parsimonious covering approach
Author(s) -
Dasigi Venu
Publication year - 1994
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550090703
Subject(s) - computer science , heuristics , artificial intelligence , natural language processing , transitive relation , inference , dual (grammatical number) , context (archaeology) , linguistics , mathematics , philosophy , paleontology , combinatorics , biology , operating system
Abductive inferences are commonplace during natural language processing. Having identified some limitations of an existing parsimonious covering model of abductive diagnostic inference, we developed an extended, dual‐route version to address issues in word sense disambiguation and logical form generation. the details of representing knowledge in this framework and the syntactic route of covering are described in a companion article [V. Dasigi, Int. J. Intell. Syst ., 9 , 571‐608 (1994)]. Here, we describe the semantic covering process in detail. A dual‐route algorithm that integrates syntactic and semantic covering is given. Taking advantage of the “transitivity” of irredundant syntactic covering, plausible semantic covers are searched for, based on some heuristics, in the space of irredundant syntactic covers. Syntactic covering identifies all possible candidates for semantic covering, which in turn, helps focus syntactic covering. Attributing both syntactic and semantic facets to “open‐class” linguistic concepts makes this integration possible. an experimental prototype has been developed to provide a proof‐of‐concept for these ideas in the context of expert system interfaces. the prototype has at least some ability to handle ungrammatical sentences, to perform some nonmonotonic inferences, etc. We believe this work provides a starting point for a nondeductive inference method for logical form generation, exploiting the associative linguistic knowledge. © 1994 John Wiley & Sons, Inc.

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