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<Paper uid="C00-1009">
  <Title>COMBINATION OF N-GRAMS AND STOCHASTIC CONTEXT-FREE GRAMMARS FOR LANGUAGE MODELING*</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This t)al)t;r de, scribes a hybrid prol)osal to combine n-grams and Stochastic Context-Free Grammars (SCFGs) tbr language modeling. A classical n-gram model is used to cat)lure the local relations between words, while a stochastic grammatical inodel is considered to represent the hmg-term relations between syntactical stru(:tm'es. In order to define this granmlatical model, which will 1)e used on large-vo(:almlary comph'~x tasks, a eategory-t)ased SCFG and a prol)abilisti(&amp;quot; model of' word (tistrilmtion in the categories have been 1)rol)osed. Methods for leanfing these stochastic models tTor complex tasks are described, and algorithms for con&gt; puting the word transition probal)ilities are also 1)resented. Filmily, ext)erilnents using the Penn Treel)ank corpus improved by 30% the test; set; l)erph~xity with regard to the classical n-gram models.</Paragraph>
  </Section>
class="xml-element"></Paper>