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  • × author_ss:"Stede, M."
  • × theme_ss:"Computerlinguistik"
  1. Stede, M.: Lexicalization in natural language generation : a survey (1994/95) 0.00 
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    Abstract
    In natural language generation, a meaning representation of some kind is successively transformed into a sentence or a text. Naturally, a central subtask of this problem is the choice of words, or lexicalization. Proposes 4 major issues that determine how a generator tackles lexicalization, and surveys the contributions that research have made to them. Identifies open problems, and sketches a possible direction for research
    Type
    a
  2. Klein, A.; Weis, U.; Stede, M.: ¬Der Einsatz von Sprachverarbeitungstools beim Sprachenlernen im Intranet (2000) 0.00 
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    Type
    a
  3. Stede, M.: Lexicalization in natural language generation (2002) 0.00 
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    Abstract
    Natural language generation (NLG), the automatic production of text by Computers, is commonly seen as a process consisting of the following distinct phases: Obviously, choosing words is a central aspect of generatiog language. In which of the these phases it should take place is not entirely clear, however. The decision depends an various factors: what exactly is seen as an individual lexical item; how the relation between word meaning and background knowledge (concepts) is defined; how one accounts for the interactions between individual lexical choices in the Same sentence; what criteria are employed for choosing between similar words; whether or not output is required in one or more languages. This article surveys these issues and the answers that have been proposed in NLG research. For many applications of natural language processing, large scale lexical resources have become available in recent years, such as the WordNet database. In language generation, however, generic lexicons are not in use yet; rather, almost every generation project develops its own format for lexical representations. The reason is that the entries of a generation lexicon need their specific interfaces to the Input representations processed by the generator; lexical semantics in an NLG lexicon needs to be tailored to the Input. Ort the other hand, the large lexicons used for language analysis typically have only very limited semantic information at all. Yet the syntactic behavior of words remains the same regardless of the particular application; thus, it should be possible to build at least parts of generic NLG lexical entries automatically, which could then be used by different systems.
    Type
    a

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