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<Paper uid="P99-1075">
  <Title>Packing of Feature Structures for Efficient Unification of Disjunctive Feature Structures</Title>
  <Section position="5" start_page="582" end_page="583" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> The packing method I described in this paper automatically extracts equivalent parts from feature structures and collapses them into a single packed feature structure. It reduces redundant repetition of unification operations on the  shows the number of lexical entries assigned to the word. Naive shows the time for unification with a naive method. PFS shows the time for unification of packed feature structures (PFS). Improvement shows the ratio ( gaive)/( PFS).</Paragraph>
    <Paragraph position="1"> Test data # of LEs Naive (msec.) PFS (msec.) Improvement (factor) credited 37 36.5 5.7 6.4 walked 79 77.2 9.2 8.4  unification operations in the naive unification algorithm (corresponds to NODE_UNIFY of my algorithm). NODE_UNIFY and SEGMENT_UNIFY are specified in Figure 6. Test data Naive NODE_UNIFY SEGMENT_UNIFY credited 30929 256 5095 walked 65709 265 10603 equivalent parts. I implemented this method in LiLFeS, and achieved a speed-up of the unification process by a factor of 6.4 to 8.4. For realizing efficient NLP systems, I am currently building an efficient parser by integrating the packing method with the compilation method for HPSG (Torisawa and Tsujii, 1996). While the compilation method reduces the number of unification operations during parsing, it cannot prevent inefficiency caused by ambiguity. The packing method will overcome this problem, and will hopefully enable us to realize practical and efficient NLP systems.</Paragraph>
  </Section>
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