Doctoral Dissertation Research: Morphological Decomposition in Arabic

点击翻译按钮获取中文摘要

When we hear a word, many factors affect the speed and accuracy with which we decide that what we have heard is indeed a word of our language. Word frequency is a particularly salient predictor of a listener's behavior when presented with a word; words which are more frequent are recognized more quickly than words which are less frequent. Moreover, the frequency of individual word parts such as suffixes and prefixes also have an effect on the recognition of the word as a whole. These phenomena are important for psycholinguistic models of storage and processing which represent how people acquire words of a language, store them in their minds, and retrieve them when they must perceive or produce them. Such models inform effective methods for teaching language as well as diagnosis and treatment of language disorders. However, studies focusing on the role of frequency in word recognition have been largely limited to Indo-European languages, which results in an incomplete picture of the mechanisms which allow humans to learn, recognize and produce language. The main goal of this psycholinguistic research is to explore the role of frequency in word recognition for a colloquial dialect of Arabic spoken in Amman, Jordan. This research entails psycholinguistic experiments measuring reactions to a variety of kinds of spoken words which differ based on the frequencies of the parts that they contain. Furthermore, these studies involve the collection of personal linguistic histories, as well as the gathering of texts and transcripts from Web, chat, and broadcast domains. This research will contribute to a richer understanding of how Arabic speakers determine what constitutes a word of their dialect, and what properties of the word affect the speed and accuracy with which they do so. Determining which word parts contribute most to their storage and access in the mind will result in more informed ways of teaching colloquial Arabic dialects to non-native speakers. Robust data focusing on which words actually belong to this particular dialect will contribute to dictionary-writing and lexicon-building, strengthening the scarce resources which exist for spoken Arabic dialects.