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<Paper uid="C00-2102">
  <Title>Named Entity Chunking Techniques in Supervised Learning for Japanese Named Entity Recognition</Title>
  <Section position="8" start_page="1510" end_page="1510" type="evalu">
    <SectionTitle>
5 Experimental Evaluation
</SectionTitle>
    <Paragraph position="0"> We experimentally evaluate the performance of the supervised learning tbr Japanese nalned entity recognition on the IREX workshop's training and test data. We compare the resuits of the confl)inations of the two encoding schemes of named entity chunking states (the Inside/Outside and the Start/End encoding schemes) and the two at)preaches to contextual feature design (the 3-gram and the Variable Length models). For each of those combinations, we search tbr an optintal threshold of the log of likelihood ratio in the decision list. The performance of each combination measured by F-measure (fl = 1) is given in Table 4.</Paragraph>
    <Paragraph position="1"> In this ewduation, we exclude the named entities with &amp;quot;other boundary mismatch&amp;quot; in Tat)le 2. We also classify the system output according to the number of constitnent lnorphemes of each named entity and evaluate the peribnnance tbr each sub-set of the system output. For each sub- null set, we compare,' the performmme of the fore' combinations of {3-grmn, Vm'iable Length} x {Inside/Outside, S|;~n't/EIld} mM show the highest tmrtbrmance with bold-faced font.</Paragraph>
    <Paragraph position="2"> Several remarkable points of these re, suits of 1)erfbrmance comparison can be stated as below: * Among the four coml)inations, the Variable Length Model with hlside/()utside Ent:oding 1)erfi)rms best in tot~fl (n &gt; 1) as well as in the recognition of named entities consisting of more thml one morl)heme (',, -2, 3, n &gt; 2, 3).</Paragraph>
    <Paragraph position="3"> * in the re,(:ognil;ion of ilsAll(;d elll;ities consisting of more than two mOl&amp;quot;l)henles (~, = 3: ?t ~ 3, 4)~ the Vm'ial)le Lellgth Model l)erforlllS signific;mtly t)etter thml the 3- rill &amp;quot;t(.~ {~l'alll mo(le\]. .tn\],' result (:letu'ly SUpl)orts the (;l~iin that our modeli\]xg of the Vm'inl)le Length Model has an adva,ntnge in the recognition ()f long named entities.</Paragraph>
    <Paragraph position="4"> &amp;quot; Ill general, the Inside/Outside en(:oding scheme l)erfol'lns slightly t)etl;er th;m the Sta\]'t/l'3nd encoding s(:henm, (Well though the tbrmer distinguislms (:onsidera|)ly ti~wer sl;ates th;m the latter.</Paragraph>
  </Section>
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