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<Paper uid="H05-1104">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 827-834, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Parallelism in Coordination as an Instance of Syntactic Priming: Evidence from Corpus-based Modeling</Title>
  <Section position="4" start_page="828" end_page="829" type="metho">
    <SectionTitle>
3 Experiment 1: Parallelism in
</SectionTitle>
    <Paragraph position="0"> Coordination In this section, we investigate the use of Church's adaptation metrics to measure the effect of syntactic parallelism in coordinated constructions. For the sake of comparison, we restrict our study to several constructions used in Frazier et al. (2000). All of these constructions occur in NPs with two coordinate sisters, i.e., constructions such as NP1 CC NP2, where CC represents a coordinator such as and.</Paragraph>
    <Section position="1" start_page="828" end_page="829" type="sub_section">
      <SectionTitle>
3.1 Method
</SectionTitle>
      <Paragraph position="0"> The application of the adaptation metric is straightforward: we pick NP1 as the prime set and NP2 as the target set. Instead of measuring the frequency of lexical elements, we measure the frequency of the following syntactic constructions:  SBAR An NP with a relative clause, i.e., NP ! NP SBAR.</Paragraph>
      <Paragraph position="1"> PP An NP with a PP modifier, i.e., NP ! NP PP. NN An NP with a single noun, i.e., NP ! NN. DT NN An NP with a determiner and a noun, i.e., NP ! DT NN.</Paragraph>
      <Paragraph position="2"> DT JJ NN An NP with a determiner, an adjective  and a noun, i.e., NP ! DT JJ NN.</Paragraph>
      <Paragraph position="3"> Parameter estimation is accomplished by iterating through the corpus for applications of the rule NP !NP CC NP. From each rule application, we create a list of prime-target pairs. We then estimate adaptation probabilities for each construction, by counting the number of prime-target pairs in which the  the WSJ corpus construction does or does not occur. This is done similarly to the document half case described above. There are four frequencies of interest, but now they refer to the frequency that a particular construction (rather than a word) either occurs or does not occur in the prime and target set.</Paragraph>
      <Paragraph position="4"> To ensure results were general across genres, we used all three parts of the English Penn Treebank: the Wall Street Journal (WSJ), the balanced Brown corpus of written text (Brown) and the Switchboard corpus of spontaneous dialog. In each case, we use the entire corpus.</Paragraph>
      <Paragraph position="5"> Therefore, in total, we report 30 probabilities: the prior and positive adaptation for each of the five constructions in each of the three corpora. The primary objective is to observe the difference between the prior and positive adaptation for a given construction in a particular corpus. Therefore, we also perform a kh2 test to determine if the difference between these two probabilities are statistically significant.</Paragraph>
    </Section>
    <Section position="2" start_page="829" end_page="829" type="sub_section">
      <SectionTitle>
3.2 Results
</SectionTitle>
      <Paragraph position="0"> The results are shown in Figure 1 for the Brown corpus, Figure 2 for the WSJ and Figure 3 for Switchboard. Each figure shows the prior and positive adaptation for all five constructions: relative clauses (SBAR) a PP modifier (PP), a single common noun (N), a determiner and noun (DT N), and a determiner adjective and noun (DT ADJ N). Only in the case of a single common noun in the WSJ and Switchboard corpora is the prior probability higher than the positive adaptation. In all other cases, the probability of the given construction is more likely to occur in NP2 given that it has occurred in NP1. According to the kh2 tests, all differences between priors and positive adaptations were significant at the 0.01 level. The size of the data sets means that even small differences in probability are statistically significant. All differences reported in the remainder of this paper are statistically significant; we omit the details of individual kh2 tests.</Paragraph>
    </Section>
    <Section position="3" start_page="829" end_page="829" type="sub_section">
      <SectionTitle>
3.3 Discussion
</SectionTitle>
      <Paragraph position="0"> The main conclusion we draw is that the parallelism effect in corpora mirrors the ones found experimentally by Frazier et al. (2000), if we assume higher probabilities are correlated with easier human processing. This conclusion is important, as the experiments of Frazier et al. (2000) only provided evidence for parallelism in comprehension data. Corpus data, however, are production data, which means that the our findings are first ones to demonstrate parallelism effects in production.</Paragraph>
      <Paragraph position="1"> The question of the relationship between comprehension and production data is an interesting one.  We can expect that production data, such as corpus data, are generated by speakers through a process that involves self-monitoring. Written texts (such as the WSJ and Brown) involve proofreading and editing, i.e., explicit comprehension processes. Even the data in a spontaneous speech corpus such as Swtichboard can be expected to involve a certain amount of self-monitoring (speakers listen to themselves and correct themselves if necessary). It follows that it is not entirely unexpected that similar effects can be found in both comprehension and production data.</Paragraph>
    </Section>
  </Section>
  <Section position="5" start_page="829" end_page="831" type="metho">
    <SectionTitle>
4 Experiment 2: Parallelism in Documents
</SectionTitle>
    <Paragraph position="0"> The results in the previous section showed that the parallelism effect, which so far had only been demonstrated in comprehension studies, is also attested in corpora, i.e., in production data. In the present experiment, we will investigate the mechanisms underlying the parallelism effect. As discussed in Section 1, there are two possible explana- null tion for the effect: one in terms of a construction-specific copying mechanism, and one in terms of a generalized syntactic priming mechanism. In the first case, we predict that the parallelism effect is restricted to coordinate structures, while in the second case, we expect that parallelism (a) is independent of coordination, and (b) occurs in the wider discourse, i.e., not only within sentences but also between sentences. null</Paragraph>
    <Section position="1" start_page="829" end_page="831" type="sub_section">
      <SectionTitle>
4.1 Method
</SectionTitle>
      <Paragraph position="0"> The method used was the same as in Experiment 1 (see Section 3.1), with the exception that the prime set and the target set are no longer restricted to being the first and second conjunct in a coordinate structure. We investigated three levels of granularity: within sentences, between sentences, and within documents. Within-sentence parallelism occurs when the prime NP and the target NP occur within the same sentence, but stand in an ar- null bitrary structural relationship. Coordinate NPs were excluded from this analysis, so as to make sure that any within-sentence parallelism is not confounded coordination parallelism as established in Experiment 1. Between-sentence parallelism was measured by regarding as the target the sentence immediately following the prime sentence. In order to investigate within-document parallelism, we split the documents into equal-sized halves; then the adaptation probability was computed by regarding the first half as the prime and the second half as the target (this method is the same as Church's method for measuring lexical adaptation).</Paragraph>
      <Paragraph position="1"> The analyses were conducted using the Wall Street Journal and the Brown portion of the Penn Treebank. The document boundary was taken to be the file boundary in these corpora. The Switchboard corpus is a dialog corpus, and therefore needs to be treated differently: turns between speakers rather  than sentences should be level of analysis. We will investigate this separately in Experiment 3 below.</Paragraph>
    </Section>
  </Section>
  <Section position="6" start_page="831" end_page="831" type="metho">
    <SectionTitle>
4.2 Results
</SectionTitle>
    <Paragraph position="0"> The results for the within-sentence analysis are graphed in Figures 4 and 5 for the Brown and WSJ corpus, respectively. We find that there is a parallelism effect in both corpora, for all the NP types investigated. Figures 6-9 show that the same is true also for the between-sentence and within-document analysis: parallelism effects are obtained for all NP types and for both corpora, even it the parallel structures occur in different sentences or in different document halves. (The within-document probabilities for the Brown corpus (in Figure 8) are close to one in most cases; the differences between the prior and adaptation are nevertheless significant.) In general, note that the parallelism effects uncovered in this experiment are smaller than the effect demonstrated in Experiment 1: The differences between the prior probabilities and the adaptation probabilities (while significant) are markedly smaller than those uncovered for parallelism in co-ordinate structure.2</Paragraph>
    <Section position="1" start_page="831" end_page="831" type="sub_section">
      <SectionTitle>
4.3 Discussion
</SectionTitle>
      <Paragraph position="0"> This experiment demonstrated that the parallelism effect is not restricted to coordinate structures.</Paragraph>
      <Paragraph position="1"> Rather, we found that it holds across the board: for NPs that occur in the same sentence (and are not part of a coordinate structure), for NPs that occur in adjacent sentences, and for NPs that occur in different document halves. The between-sentence effect has been demonstrated in a more restricted from by Gries (2005) and Szmrecsanyi (2005), who investigate priming in corpora for cases of structural choice (e.g., between a dative object and a PP object for verbs like give). The present results extend this finding to arbitrary NPs, both within and between sentences. null The fact that parallelism is a pervasive phenomenon, rather than being limited to coordinate structures, strongly suggests that it is an instance of a general syntactic priming mechanism, which has been an established feature of accounts of the human sentence production system for a while (e.g., Bock, 2The differences between the priors and adaptation probabilities are also much smaller than noted by Church (2000). The probabilities of the rules we investigate have a higher marginal probability than the lexical items of interest to Church. 1986). This runs counter to the claims made by Frazier et al. (2000) and Frazier and Clifton (2001), who have argued that parallelism only occurs in coordinate structures, and should be accounted for using a specialized copying mechanism. (It is important to bear in mind, however, that Frazier et al. only make explicit claims about comprehension, not about production.) null However, we also found that parallelism effects are clearly strongest in coordinate structures (compare the differences between prior and adaptation in Figures 1-3 with those in Figures 4-9). This could explain why Frazier et al.'s (2000) experiments failed to find a significant parallelism effect in non-coordinated structures: the effect is simply too week to detect (especially using the self-paced reading paradigm they employed).</Paragraph>
    </Section>
  </Section>
  <Section position="7" start_page="831" end_page="832" type="metho">
    <SectionTitle>
5 Experiment 3: Parallelism in
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="831" end_page="832" type="sub_section">
      <SectionTitle>
Spontaneous Dialog
</SectionTitle>
      <Paragraph position="0"> Experiment 1 showed that parallelism effects can be found not only in written corpora, but also in the Switchboard corpus of spontaneous dialog. We did not include Switchboard in our analysis in Experiment 2, as this corpus has a different structure from the two text corpora we investigated: it is organized in terms of turns between two speakers. Here, we exploit this property and conduct a further experiment in which we compare parallelism effects between speakers and within speakers.</Paragraph>
      <Paragraph position="1"> The phenomenon of structural repetition between speakers has been discussed in the experimental psycholinguistic literature (see Pickering and Garrod 2004 for a review). According to Pickering and Garrod (2004), the act of engaging in a dialog facilitates the use of similar representations at all linguistic levels, and these representations are shared between speech production and comprehension processes. Thus structural adaptation should be observed in a dialog setting, both within and between speakers. An alternative view is that production and comprehension processes are distinct. Bock and Loebell (1990) suggest that syntactic priming in speech production is due to facilitation of the retrieval and assembly procedures that occur during the formulation of utterances. Bock and Loebell point out that this production-based procedural view predicts a lack of priming between comprehension and production or vice versa, on the assumption that</Paragraph>
    </Section>
    <Section position="2" start_page="832" end_page="832" type="sub_section">
      <SectionTitle>
Switchboard corpus
</SectionTitle>
      <Paragraph position="0"> production and parsing use distinct mechanisms. In our terms, it predicts that between-speaker positive adaptation should not be found, because it can only result from priming from comprehension to production, or vice versa. Conversely, the prodedural view outlined by Bock and Loebell predicts that positive adaptation should be found within a given speaker's dialog turns, because such adaptation can indeed be the result of the facilitation of production routines within a given speaker.</Paragraph>
    </Section>
    <Section position="3" start_page="832" end_page="832" type="sub_section">
      <SectionTitle>
5.1 Method
</SectionTitle>
      <Paragraph position="0"> We created two sets of prime and target data to test within-speaker and between-speaker adaptation.</Paragraph>
      <Paragraph position="1"> The prime and target sets were defined in terms of pairs of utterances. To test between-speaker adaptation, we took each adjacent pair of utterances spoken by speaker A and speaker B, in each dialog, and these were treated as prime and target sets respectively. In the within-speaker analysis, the prime and target sets were taken from the dialog turns of only one speaker--we took each adjacent pair of dialog turns uttered by a given speaker, excluding the intervening utterance of the other speaker. The earlier utterance of the pair was treated as the prime, and the later utterance as the target. The remainder of the method was the same as in Experiments 1 and 2 (see Section 3.1).</Paragraph>
    </Section>
  </Section>
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