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<Paper uid="W04-3241">
  <Title>The Entropy Rate Principle as a Predictor of Processing Effort: An Evaluation against Eye-tracking Data</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> This paper made a contribution to the understanding of the entropy rate principle, first proposed by Genzel and Charniak (2002). This principle predicts that the position of a sentence in a text should correlate with its entropy, defined as the sentence probability normalized by sentence length. In Experiment 1, we replicated the entropy rate effect reported by Genzel and Charniak (2002, 2003) and showed that it generalizes to a larger range of sentence positions and also holds for individual sentences, not just averaged over all sentences with the same position. However, we also found that a simple baseline model based on sentence length achieves a correlation with sentence position. In many cases, there was no significant difference between the entropy rate model and the baseline. This raises the possibility that the entropy rate effect is simply an artifact of the way entropy rate is computed, which involves sentence length as a normalizing factor. However, using partial correlation analysis, we were able to show that entropy is a significant predictor of sentence position, even when sentence length is controlled. null In Experiment 2, we tested a number of important predictions of the entropy rate principle for human sentence processing. First, we replicated the entropy rate effect on a different corpus, a subset of the BNC restricted to newspaper text. We found essentially the same pattern as in Experiment 1. Using a corpus of eye-tracking data, we showed that entropy is correlated with processing difficulty, as measured by reading times in the eye-movement record. This confirms an important assumption that underlies the entropy rate principle. As the eye-tracking corpus we used was a corpus of connected sentences, it enabled us to also test another prediction of the entropy rate principle: in context, all sentences should be equally difficult to process, as speakers generate sentences with constant informativeness. This means that no correlation between sentence position and reading times was expected, which is what we found.</Paragraph>
    <Paragraph position="1"> Another important prediction of the entropy rate principle remains to be evaluated in future work: for out-of-context sentences, there should be a correlation between sentence position and processing effort. This prediction can be tested by obtaining reading times for sentences sampled from a corpus and read by experimental subjects in isolation.</Paragraph>
  </Section>
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