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<Paper uid="A94-1037">
  <Title>Spelling Correction in Agglutinative Languages</Title>
  <Section position="4" start_page="194" end_page="194" type="metho">
    <SectionTitle>
EXAMPLE
</SectionTitle>
    <Paragraph position="0"> Misspelled t:word: qalSSrnalamyla Threshold : 2 Solutions: yamSSmalamyla yatlSSmalanyla on left edge yaplSSmalarlyla yaklSSmalarlyla iC/~,SSmalamyla qlk1SSrnalarlyla Candidate lq.oots:46a~ qakl 6al qah qam qan 6ap gar qat qatl qag qak qaklSS qal qahSS ~ap qat qatlSS qav ~av qay Solutions: 5 Lexical Surface  our algorithm on a set of 141 randomly selected incorrect words from Turkish text with edit distances 1 (86%) and 2 (14%) to their correct form. The morphological analyzer and generator that we used was our two-level specification for Turkish (Oflaser, 1993), developed using the PC-KIMMO system. It is, however, rather slow and can analyze only about 2 forms per second and can generate about 50 forms a second on Sun Sparcstations. So, instead of using timings, we counted the number of times certain expensive operations we called. The statistics shown in Table 1 show the average number of morphological recognitions and generations, and the edit distance operations required, and the number of correct solutions offered per misspelled input word. The last column indicates the percentage of cases the intended correct form was found. We also have developed a algorithm for ranking the solutions which offers the intended correct form as the first in 74% of the cases when t = 1.</Paragraph>
  </Section>
  <Section position="5" start_page="194" end_page="194" type="metho">
    <SectionTitle>
4 Conclusions
</SectionTitle>
    <Paragraph position="0"> This poster has presented a spelling correction algorithm for agglutinative languages that is based on a two-level morphological generator and analyzer, and a intelligent generate and test search procedure.</Paragraph>
  </Section>
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