<?xml version="1.0" standalone="yes"?>
<Paper uid="P94-1027">
  <Title>OPTIMIZING THE COMPUTATIONAL LEXICALIZATION OF LARGE GRAMMARS</Title>
  <Section position="3" start_page="202" end_page="202" type="concl">
    <SectionTitle>
Conclusion
</SectionTitle>
    <Paragraph position="0"> As mentioned in the introduction, the improvement of the lexicalization through an optimization algorithm is currently used in FASTR a parser for terminological extraction through NLP techniques where terms are represented by lexicalized rules. In this framework as in top-down parsing with LTAGs (Schabes and Joshi, 1990), the first phase of parsing is a filtering of the rules with their anchors in the input sentence. An unbalanced distribution of the rules on to the lexical items has the major computational drawback of selecting an excessive number of rules when the input sentence includes a common head word such as &amp;quot;'alloy&amp;quot; (127 rules have &amp;quot;alloy&amp;quot; as head). The use of the optimized lexicalization allows us to filter 57% of the rules selected by the linguistic lexicalization. This reduction is comparable to the filtering induced by linguistic lexicalization which is around 85% (Schabes and Joshi, 1990).</Paragraph>
    <Paragraph position="1"> Correlatively the parsing speed is multiplied by 2.6 confirming the computational saving of the optimization reported in this study.</Paragraph>
    <Paragraph position="2"> There are many directions in which this work could be refined and extended. In particular, an optimization of this optimization could be achieved by testing different weight assignments in correlation with the parsing algorithm. Thus, the computational lexicalization would fasten both the preprocessing and the parsing algorithm.</Paragraph>
  </Section>
class="xml-element"></Paper>