Kathleen r. mckeown

Xem 1-5 trên 5 kết quả Kathleen r. mckeown
  • We present a method to automatically generate a concise s u m m a r y by identifying and synthesizing similar elements across related text from a set of multiple documents. Our approach is unique in its usage of language generation to reformulate the wording of the summary. Information overload has created an acute need for summarization. Typically, the same information is described by many different online documents.

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  • One problem for the generation of natural language text is determining when to use a sequence of simple sentences and when a single complex one is more appropriate. In this paper, we show how focus of attention is one factor that influences this decision and describe its implementation in a system that generates explanations for a student advisor expert system.

    pdf8p bungio_1 03-05-2013 35 2   Download

  • While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases.

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  • In a language generation system, a content planner embodies one or more “plans” that are usually hand–crafted, sometimes through manual analysis of target text. In this paper, we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them. As training data, we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic.

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  • In this paper, we show that one benefit of FUG, the ability to state global conslralnts on choice separately from syntactic rules, is difficultin generation systems based on augmented context free grammars (e.g., Def'mite Clause Cn'anmm~). They require that such constraints be expressed locally as part of syntactic rules and therefore,duplicated in the grammar. Finally, we discuss a reimplementation of l U g that achieves the similar levels of efficiency as Rubinoff's adaptation of M U M B L E , a detcrministc language generator. ...

    pdf7p bungio_1 03-05-2013 26 1   Download



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