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<Paper uid="P06-3005">
  <Title>Modeling Human Sentence Processing Data with a Statistical Parts-of-Speech Tagger</Title>
  <Section position="5" start_page="26" end_page="28" type="metho">
    <SectionTitle>
3 Results
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="26" end_page="26" type="sub_section">
      <SectionTitle>
3.1 The Probability Decrease per Word
</SectionTitle>
      <Paragraph position="0"> Unambiguous sentences are usually longer than garden-path sentences. To compare sentences of different lengths, the joint probability of the whole sentence and tags was divided by the number of words in the sentence. The result showed that the average probability decrease was greater in garden-path sentences compared to their unambiguous control sentences. This indicates that garden-path sentences are more difficult than un-ambiguous sentences, which is consistent with empirical findings.</Paragraph>
      <Paragraph position="1"> Probability decreased faster in object-relative sentences than in subject relatives as predicted.</Paragraph>
      <Paragraph position="2"> In the psycholinguistics literature, the comparative difficulty of object-relative clauses has been explained in terms of verbal working memory (King and Just, 1991), distance between the gap and the filler (Bever and McElree, 1988), or perspective shifting (MacWhinney, 1982). However, the test results in this study provide a simpler account for the effect. That is, the comparative difficulty of an object-relative clause might be attributed to its less frequent POS sequence. This account is particularly convincing since each pair of sentences in the experiment share the exactly same set of words except their order.</Paragraph>
    </Section>
    <Section position="2" start_page="26" end_page="26" type="sub_section">
      <SectionTitle>
3.2 Probability Decrease at the
Disambiguating Region
</SectionTitle>
      <Paragraph position="0"> A total of 30 pairs of a garden-path sentence and its ambiguous, non-garden-path control were tested for a comparison of the probability decrease at the disambiguating region. In 80% of the cases, the probability drops more sharply in garden-path sentences than in control sentences at the critical word. The test results are presented in (9) with the number of test sets for each ambiguous type and the number of cases where the model correctly predicted reading-time penalty of garden-path sentences. null  The two graphs in Figure 1 illustrate the comparison of probability decrease between a pair of sentence. The y-axis of both graphs in Figure 1 is log probability. The first graph compares the probability drop for PP ambiguity (Katie put the dress on the floor and/onto the bed....) The empirical result for this type of ambiguity shows that reading time penalty is observed when the second PP, onto the bed, is introduced, and there is no such effect for the other sentence. Indeed, the sharper probability drop indicates that the additional PP is less likely, which makes a prediction of a comparative processing difficulty. The second graph exhibits the probability comparison for the DO/SC ambiguity. The verb forget is a DO-biased verb and thus processing difficulty is observed when it has a sentential complement. Again, this effect was replicated here.</Paragraph>
      <Paragraph position="1"> The results showed that the disambiguating word given the previous context is more difficult in garden-path sentences compared to control sentences. There are two possible explanations for the processing difficulty. One is that the POS sequence of a garden-path sentence is less probable than that of its control sentence. The other account is that the disambiguating word in a garden-path sentence is a lower frequency word compared to that of its control sentence.</Paragraph>
      <Paragraph position="2"> For example, slower reading time was observed in (10a) and (11a) compared to (10b) and (11b) at the disambiguating region that is bolded.</Paragraph>
      <Paragraph position="3">  (10) Different POS at the Disambiguating Region a. Katie laid the dress on the floor onto ([?]57.80) the bed.</Paragraph>
      <Paragraph position="4"> b. Katie laid the dress on the floor after ([?]55.77) her mother yelled at her.</Paragraph>
      <Paragraph position="5"> (11) Same POS at the Disambiguating Region a. The umpire helped the child on ([?]42.77) third base.</Paragraph>
      <Paragraph position="6"> b. The umpire helped the child to ([?]42.23) third base.</Paragraph>
      <Paragraph position="7">  The log probability for each disambiguating word is given at the end of each sentence. As expected, the probability at the disambiguating region in (10a) and (11a) is lower than in (10b) and (11b) respectively. The disambiguating words in (10) have different POS's; Preposition in (10a) and Conjunction (10b). This suggests that the probabilities of different POS sequences can account for different reading time at the region. In (11), however, both disambiguating words are the same POS (i.e. Preposition) and the POS sequences for both sentences are identical. Instead, &amp;quot;on&amp;quot; and &amp;quot;to&amp;quot;, have different frequencies and this information is reflected in the conditional probability P(wordi|state). Therefore, the slower reading time in (11b) might be attributable to the lower frequency of the disambiguating word, &amp;quot;to&amp;quot; compared to &amp;quot;on&amp;quot;.</Paragraph>
    </Section>
    <Section position="3" start_page="26" end_page="28" type="sub_section">
      <SectionTitle>
3.3 Probability Re-ranking
</SectionTitle>
      <Paragraph position="0"> The probability re-ranking reported in Corley and Crocker (2000) was replicated. The tagger successfully resolved the ambiguity by reanalysis when the ambiguous word was immediately followed by the disambiguating word (e.g. Without her he was lost.). If the disambiguating word did not immediately follow the ambiguous region, (e.g. Without her contributions would be very inadequate.) the ambiguity is sometimes incorrectly resolved.</Paragraph>
      <Paragraph position="1"> When revision occurred, probability dropped more sharply at the revision point and at the disambiguation region compared to the control sen- null tences. When the ambiguity was not correctly resolved, the probability comparison correctly modeled the comparative difficulty of the garden-path sentences Of particular interest in this study is RR ambiguity resolution. The tagger predicted the processing difficulty of the RR ambiguity with probability re-ranking. That is, the tagger initially favors the main-verb interpretation for the ambiguous -ed form, and later it makes a repair when the ambiguity is resolved as a past-participle.</Paragraph>
      <Paragraph position="2"> The RR ambiguity is often categorized as a syntactic ambiguity, but the results suggest that the ambiguity can be resolved locally and its processing difficulty can be detected by a finite state model. This suggests that we should be cautious in assuming that a structural explanation is needed for the RR ambiguity resolution, and it could be that similar cautions are in order for other ambiguities usually seen as syntactic.</Paragraph>
    </Section>
  </Section>
  <Section position="6" start_page="28" end_page="28" type="metho">
    <SectionTitle>
4 Discussion
</SectionTitle>
    <Paragraph position="0"> The current study explores Corley and Crocker's model(2000) further on the model's account of human sentence processing data seen in empirical studies. Although there have been studies on a POS tagger evaluating it as a potential cognitive module of lexical category disambiguation, there has been little work that tests it as a modeling tool of syntactically ambiguous sentence processing.</Paragraph>
    <Paragraph position="1"> The findings here suggest that a statistical POS tagging system is more informative than Crocker and Corley demonstrated. It has a predictive power of processing delay not only for lexically ambiguous sentences but also for structurally garden-pathed sentences. This model is attractive since it is computationally simpler and requires few statistical parameters. More importantly, it is clearly defined what predictions can be and cannot be made by this model. This allows systematic testability and refutability of the model unlike some other probabilistic frameworks. Also, the model training and testing is transparent and observable, and true probability rather than transformed weights are used, all of which makes it easy to understand the mechanism of the proposed model.</Paragraph>
    <Paragraph position="2"> Although the model we used in the current study is not a novelty, the current work largely differs from the previous study in its scope of data used and the interpretation of the model for human sentence processing. Corley and Crocker clearly state that their model is strictly limited to lexical ambiguity resolution, and their test of the model was bounded to the noun-verb ambiguity. However, the findings in the current study play out differently. The experiments conducted in this study are parallel to empirical studies with regard to the design of experimental method and the test material. The garden-path sentences used in this study are authentic, most of them are selected from the cited literature, not conveniently coined by the authors. The word-by-word probability comparison between garden-path sentences and their controls is parallel to the experimental design widely adopted in empirical studies in the form of regionby-region reading or eye-gaze time comparison.</Paragraph>
    <Paragraph position="3"> In the word-by-word probability comparison, the model is tested whether or not it correctly predicts the comparative processing difficulty at the garden-path region. Contrary to the major claim made in previous empirical studies, which is that the garden-path phenomena are either modeled by syntactic principles or by structural frequency, the findings here show that the same phenomena can be predicted without such structural information.</Paragraph>
    <Paragraph position="4"> Therefore, the work is neither a mere extended application of Corley and Crocker's work to a broader range of data, nor does it simply confirm earlier observations that finite state machines might accurately account for psycholinguistic results to some degree. The current study provides more concrete answers to what finite state machine is relevant to what kinds of processing difficulty and to what extent.</Paragraph>
  </Section>
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