<?xml version="1.0" standalone="yes"?>
<Paper uid="I05-2034">
  <Title>Probabilistic Models for Korean Morphological Analysis</Title>
  <Section position="3" start_page="197" end_page="197" type="intro">
    <SectionTitle>
2 Related works
</SectionTitle>
    <Paragraph position="0"> Over the past few decades, a considerable number of studies have been made on Korean morphological analysis. The early studies concentrated on the algorithmic research. The following approaches belong to this group: longest matching algorithm, tabular parsing method using CYK algorithm (Kim, 1986), dictionary based approach (Kwon, 1991), two-level morphology(Lee, 1992), and syllable-based approach (Kang and Kim, 1994).</Paragraph>
    <Paragraph position="1"> Next, many studies have been made on improving the efficiency of the morphological analyzers. There have been studies to reduce the search space and implausible interpretations by using characteristics of Korean syllables (Kang, 1995; Lim et al., 1995).</Paragraph>
    <Paragraph position="2"> There have been no standard tagset and annotation guideline, so researchers have developed methods with their own tagsets and guidelines.</Paragraph>
    <Paragraph position="3"> The Morphological Analysis and Tagger Evaluation Contest (MATEC) took place in 1999. This is the first trial about the objective and relative evaluation of morphological analysis. Among the participants, some newly implemented the systems and others converted the existing systems' results through postprocessing steps. In both cases, they reported that they spent much effort and argued the necessity of tuning the linguistic knowledge.</Paragraph>
    <Paragraph position="4"> All the systems described so far can be considered as the so called dictionary and rule based approach. In this approach, the quality of the dictionary and the rules govern the system's performance. null The proposed approach is the first attempt to probabilistic morphological analysis. The aim of the paper is to show that this approach can achieve comparable performances with the previous approaches. null</Paragraph>
  </Section>
class="xml-element"></Paper>