<?xml version="1.0" standalone="yes"?>
<Paper uid="P06-1007">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Finite-State Model of Human Sentence Processing</Title>
  <Section position="4" start_page="0" end_page="49" type="intro">
    <SectionTitle>
2 Previous Work
</SectionTitle>
    <Paragraph position="0"> Corley and Crocker (2000) present a probabilistic model of lexical category disambiguation based on a bigram statistical POS tagger. Kim et al. (2002) suggest the feasibility of modeling human syntactic processing as lexical ambiguity resolution using a syntactic tagging system called Super-Tagger  (Joshi and Srinivas, 1994; Bangalore and Joshi, 1999). Probabilistic parsing techniques also have been used for sentence processing modeling (Jurafsky, 1996; Narayanan and Jurafsky, 2002; Hale, 2001; Crocker and Brants, 2000). Jurafsky (1996) proposed a probabilistic model of HSPM using a parallel beam-search parsing technique based on the stochastic context-free grammar (SCFG) and subcategorization probabilities. Crocker and Brants (2000) used broad coverage statistical parsing techniques in their modeling of human syntactic parsing. Hale (2001) reported that a probabilistic Earley parser can make correct predictions of garden-path effects and the subject/object relative asymmetry. These previous studies have used small numbers of examples of, for example, the Reduced-relative clause ambiguity and the Direct-Object/Sentential-Complement ambiguity.</Paragraph>
    <Paragraph position="1"> The current study is closest in spirit to a previous attempt to use the technology of part-of-speech tagging (Corley and Crocker, 2000).</Paragraph>
    <Paragraph position="2"> Among the computational models of the HSPM mentioned above, theirs is the simplest. They tested a statistical bigram POS tagger on lexically ambiguous sentences to investigate whether the POS tagger correctly predicted reading-time penalty. When a previously preferred POS sequence is less favored later, the tagger makes a repair. They claimed that the tagger's reanalysis can model the processing difficulty in human's disambiguating lexical categories when there exists a discrepancy between lexical bias and resolution.</Paragraph>
  </Section>
class="xml-element"></Paper>