caa a22 4500 465752160 CHVBK 20180323111837.0 cr unu---uuuuu 170327e19900301xx s 000 0 eng 10.1007/BF00310042 doi (NATIONALLICENCE)springer-10.1007/BF00310042 How to realize a concept: Lexical selection and the conceptual network in text generation [Elektronische Daten] [Norman Sondheimer, Susanna Cumming, Robert Albano] The task of natural language generation by computer requires a method for the selection of lexical items that refer to concepts. A generation system cannot assume that every concept will have an appropriate lexical entry associated with it. Similarly, a generation system must avoid redundant text and must strive for the most specific descriptions possible. This paper presents an algorithm for lexical selection that takes advantage of a conceptual network in which we represent the relationships between concepts (such as which concepts subsume each other and what differentiates concepts at different levels of generality). When the algorithm chooses a lexical realization for a concept, it first checks to see if the concept to be expressed is associated with a lexical entry. If so, that entry is used. If the concept does not have the appropriate lexical associations, the algorithm generates a phrase with a more general head term and restrictive modifiers. It does this by using the conceptual network to compute a semantically appropriate head term, and then modifying the request for generation by adding restrictions that differentiate the concept associated with the head term from the concept initially requested. Similarly, the conceptual network is used to eliminate redundant modifiers by computing the information contained in a lexical item that is chosen as a head term. Here the algorithm modifies the generation request to avoid restating the extra information. This algorithm is being implemented within Penman, a computerized system for the generation of English text from statements in a first-order predicate calculus language. Kluwer Academic Publishers, 1990 concepts nationallicence English nationallicence first-order logic nationallicence knowledge representation nationallicence lexical selection nationallicence meaning postulates nationallicence natural-language generation nationallicence nouns nationallicence Sondheimer Norman General Electric Corporation, Research and Development, Schenectady, USA aut Cumming Susanna Linguistics Department, University of Colorado, Boulder, USA aut Albano Robert Graduate School of Architecture and Urban Planning, University of California at Los Angeles, Los Angeles, USA aut Machine Translation Kluwer Academic Publishers 5/1(1990-03-01), 57-78 0922-6567 5:1<57 1990 5 10590 https://doi.org/10.1007/BF00310042 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00310042 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sondheimer Norman General Electric Corporation, Research and Development, Schenectady, USA aut NATIONALLICENCE 700 1- Cumming Susanna Linguistics Department, University of Colorado, Boulder, USA aut NATIONALLICENCE 700 1- Albano Robert Graduate School of Architecture and Urban Planning, University of California at Los Angeles, Los Angeles, USA aut NATIONALLICENCE 773 0- Machine Translation Kluwer Academic Publishers 5/1(1990-03-01), 57-78 0922-6567 5:1<57 1990 5 10590 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer