For every participant role filler in an utterance, speakers must choose to leave it bare (e.g., "the interviewer") or to modify it (e.g., "the interviewer on Fresh Air"). Their decision is the end result of a combination of complex factors ranging from the original message to how distracted the speaker is. When we use corpora to create language models, part of our job is understanding the observable properties in and around an event description that allow us to predict these decisions. A considerable body of work on language production and discourse pragmatics concentrates on measuring noun phrase predictability and other forms of shared knowledge that help determine the balance point between over- and under-specification of a participant role filler. Although the importance of predictability as measured by long-term probabilities has long been recognized, I present a novel quantitative analysis of participant role filler predictability, the structure of the mental lexicon, and how the interaction of these two inform a speaker's internal perception of informativity. Standard Gricean assumptions tend to be efficiency oriented. Speakers will be informative enough but not wastefully so. Using these to model corpus distributions predict that noun phrase modification rates are directly proportional to predictability in order to satisfy the speaker's obligation to always be informative. In contrast, standard Firthian models (built around the idea that "you know a word by the company it keeps") assume spreading activation—and not efficiency—is the dominant predictor of usage. Sensitivity to activation's effect predicts that noun phrase modification rates are inversely proportional to predictability. Strongly connected participant role fillers could be easily activated for production while weakly connected participant role fillers would either be mentioned less often or themselves trigger strongly connected features (not normally associated with the head verb) to be primed for production.
To distinguish between these competing assumptions, I analyze participant role filler modification rates in event descriptions with respect to three indicators: the syntactic and semantic optionality of the role filler, the general predictability of the verb's role fillers, and the predictability of individual pairs of verb/participant role fillers. First, I use insights from linguistic theory to classify verbs and their participant roles into classes of syntactic optionality and semantic optionality. Second, I quantify over a large corpus the general predictability of a verb's participant roles and the specific predictability of each pair of verb/participant role filler. Finally, I model the relationship between the three indicators and modification in order to ascertain whether speakers have a stronger tendency to modify the more predictable participant role fillers, as Grice's Maxim of Relevancy predicts, or a tendency to modify the less predictable participant role fillers, as a Firthian activation-based model predicts.
I present descriptive statistical models to chart the relationship between predictability, syntactic optionality of a participant role, and semantic optionality of a participant role. In general, verb classes with stronger mental lexicon connections to their participant role fillers according to theory also have more predictable participant role fillers in the British National Corpus. Specifically, syntactically optional direct object verbs and semantically obligatory instrument verbs have more predictable participant role fillers than the opposite, comparable verb class. I also present several linear mixed-effect models to determine how predictive of modification the independent variables of syntactic verb class, semantic verb class, and verb/participant role filler predictability are. According to these models, speakers are significantly more likely to modify the less predicted participant role fillers even when taking into account individual verb and verb class differences. I conclude that mental lexicon accessibility modulates noun phrase realization according to a Firthian activation-based model. For each factor, I discuss possible explanations for the correlations between modification, predictability, and optionality and how these correlations make sense within a larger production model.
|Advisor:||Koenig, Jean-Pierre, Roland, Douglas William|
|Commitee:||Srihari, Rohini, Zubin, David|
|School:||State University of New York at Buffalo|
|School Location:||United States -- New York|
|Source:||DAI-A 76/07(E), Dissertation Abstracts International|
|Subjects:||Linguistics, Cognitive psychology, Computer science|
|Keywords:||Computational linguistics, Computational semantics, Corpus linguistics, Entropy and predictability, Semantic optionality, Syntactic optionality|
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