Natural language text processing and the maximal join operator
Conceptual graphs have proven to be a convenient representation formalism for natural language processing. In NLP, the lexical semantics of words often has to be mapped onto large knowledge bases. The most useful operation in the conceptual graphs formalism is the maximal join. This paper describes problems with ambiguities at different levels, including ambiguities resulting from the maximal join operation itself. This research was motivated by the idea to use different sources of information for text understanding. Layout information is one such source of semantic constraints. The traditional maximal join algorithm does not take into account any other constraints. In the following paper, we describe an implementation of an augmented maximal join operation. We will talk about the types of constraints and how they are integrated into the maximal join operation.
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- Fargues, Jean, Marie Claude Landau, Anne Dogourd, & Laurent Catach (1986): “Conceptual graphs for semantics and knowledge processing”; IBM J. of Res. & Develop., 30:1, 70–89Google Scholar
- Jacobs, Paul S., & Lisa F. Rau: “Innovations in text interpretation”; Artificial Intelligence 63(1993) 143–191Google Scholar
- Petermann, Heike (1994): “Automatische Generierung von Wissensrepräsentationen aus Manualquelltexten” Workshop der GI-Fachgruppe “Intelligente Lehr-/Lernsysteme”; FAW an der Universität Ulm, FAW-TR-94003, Mai 1994Google Scholar
- Petermann, Heike, Lutz Euler, & Kalina Bontcheva: “CGPro — a PROLOG Implementation of Conceptual Graphs”; Technical Report, University of Hamburg, FBI-HH-M-251/95Google Scholar
- “SNI-PROLOG V3.1A, Sprachbeschreibung”; Siemens Nixdorf Informationssysteme AG (1993)Google Scholar
- Sowa, J.F. (1984): “Conceptual Structures Information Processing in Mind and Machine”, Addison-Wesley Publishing Company, 1984Google Scholar
- John F. Sowa and Eileen C. Way (1986): “Implementing a Semantic Interpreter Using Conceptual Graphs”; IBM J. Res. Develop., 30:1Google Scholar
- John F. Sowa (1988): “Using a Lexicon of Canonical Graphs in a Semantic Interpreter” in “Relational Models of the Lexicon”, edited by Martha Evens, Cambridge University Press, 1988, 113–137Google Scholar
- Velardi, Paola, Maria Teresa Pazienza, & Mario De' Giovanetti (1988): “Conceptual Graphs for the analysis and generation of sentences”; IBM J. Res. Develop., 32:2, 251–267Google Scholar
- Wermter, S. und V. Weber (1994): “Learning Fault-tolerant Speech Parsing with SCREEN”; Twelfth National Conference on Artificial Intelligence (AAAI-94)Google Scholar