Yu-Wen Liu

This study examines how different dimensions of corpus frequency data may affect the outcome of statistical modeling of lexical items. Our analysis mainly focuses on a recently constructed elderly speaker corpus that is used to reveal patterns of aging people’s language use. A conversational corpus contributed by speakers in their 20s serves as complementary material. The target words examined are temporal expressions, which might reveal how the speech produced by the elderly is organized. We conduct divisive hierarchical clustering analyses based on two different dimensions of corporal data, namely raw frequency distribution and collocation-based vectors. When different dimensions of data were used as the input, results showed that the target terms were clustered in different ways. Analyses based on frequency distributions and collocational patterns are distinct from each other. Specifically, statistically-based collocational analysis generally produces more distinct clustering results that differentiate temporal terms more delicately than do the ones based on raw frequency. 1 Acknowledgement: Thanks Wang Chun-Chieh, Liu Chun-Jui, Anna Lofstrand, and Hsu Chan-Chia for their involvement in the construction of the elderly speakers’ corpus and the early development of this paper. ∗ Graduate Institute of Linguistics, National Taiwan University, 3F, Le-Xue Building, No. 1, Sec. 4, Roosevelt Rd., Taipei Taiwan, 106 E-mail: {sftwang0416; flower75828; june06029}@gmail.com; shukaihsieh@ntu.edu.tw + Department of English, National Taiwan Normal University, No. 162, He-ping East Road, Section 1, Taipei, Taiwan, 106 E-mail: Yw_L7@hotmail.com 38 Sheng-Fu Wang et al

This study examines how different dimensions of corpus frequency data may affect the outcome of statistical modeling of lexical items. The corpus used in our analysis is an elderly speaker corpus in its early development, and the target words are temporal expressions, which might reveal how the speech produced by the elderly is organized. We conduct divisive hierarchical clustering based on two different dimensions of corpus data, namely raw frequency distribution and collocation-based vectors. Results show when different dimensions of data were used as the input, the target terms were indeed clustered in different ways. Analyses based on frequency distributions and collocational patterns are distinct from each other. Specifically, statistically-based collocational analysis produces more distinct clustering results that differentiate temporal terms more delicately than do the ones based on raw frequency.