A Crosslinguistic Study of Sign Language Classifiers

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With National Science Foundation support, Dr. Diane Brentari will conduct three years of linguistic research on the classifier systems of nine sign languages from three different families. Such systems exist in all known sign languages, but in only some spoken languages. Classifiers refer to certain properties of noun arguments but may be expressed in a variety of grammatical units. For example, they may appear in noun phrases (e.g., "grain" in "a grain of sand" in English) or verb phrases expressing motion or location (e.g., "3-handshape + go_by" in American Sign Language; translation: "A bike is going by"). In sign languages, they are typically expressed as handshapes. This research will ask whether nine sign languages use similar handshapes to express similar meanings and how each system compares to the set of all languages that contain well-developed classifier systems, both spoken and signed. The relatively young Israeli Sign Language is included to compare to more mature classifier systems. Dr. Brentari and a linguist in each language community will collect the data. Elicitation tasks target specific semantic distinctions such as stative/active, agentive/non-agentive, and telic/atelic. Researchers will analyze how each sign language uses the components of the total handshape in its classifier system to express these distinctions. The Prosodic Model of sign language phonology will provide a theoretical framework within which to organize and analyze the data.Three scientific questions motivate this study of sign language classifiers. First, this project will contribute to our knowledge of sign languages by providing cross-linguistic information about a fundamental structure that is not yet well understood. Second, this project will add to our knowledge of morphology and the way that it is expressed, since morphology in sign languages is expressed predominantly by simultaneously organized phonological units rather than by sequentially organized units. Finally, this research will contribute to our understanding of the range of classifier typology in natural languages. In addition to its scientific merit, this project will recruit native-signing Deaf undergraduate students to help analyze data, and so provide an opportunity for these students to engage in first-hand scientific research on their native languages.