Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: The goal of natural language processing is to construct a 
        computer-digestible representation of the meaning of the typed
        sentence, i.e. a semantic representation.
        The development of larger scale natural language systems has 
        been hampered by the need to manually create mappings from 
        syntactic structures into meaning representations.
        A new approach to semantic interpretation is described, which 
        uses partial syntactic  structures as the main unit of analysis
        for interpretation rules.
        The approach can work for a variety of syntactic representations
        corresponding to directed acyclic graphs and is designed to map 
        into meaning representations based on frame hierarchies with 
        inheritance.
        Semantic interpretation rules are defined in a compact format 
        which is suitable for automatic rule extension or 
        generalization, when existing hand coded rules do not cover the
        current input. 
        Furthermore, automatic discovery of semantic interpretation 
        rules from input/output a comparison to other methods on an 
        independently developed domain.
        In experiments performed on an English language corpus of 
        sentences, the approach allowed semantic interpretation rules to
        be created manually in about 50 percent less time, with 78 
        percent coverage of the test corpus, as opposed to the 66.1 
        percent coverage which had been achieved before with the 
        original rules written for this application by independent 
        sources.
        In addition, automatic rule discovery on the English test 
        corpus.		
        Similar experiments performed on a Japanese corpus of sentences 
        yielded comparable results, with a slight disadvantage for both
        manual rule creation as well as automatic rule discovery using
        the new approach, due to external factors such as incomplete
        lexical coverage.
        Instead of relying purely on painstaking human effort, this 
        thesis shows that a combination of human expertise with learning
        strategies by the computer on representative examples is 
        successful to overcome the bottleneck of semantic 
        interpretation.
Number: CMU-CS-91-158
Bibtype: TechReport
Month: jul
Author: Alexander G. Hauptmann
Title: Meaning from Structure in Natural Language Processing
Year: 1991
Address: Pittsburgh, PA
Super: @CMUTR