Classifier systems constitute one of the most prominent features of East and South-East Asian languages (Li, 2013). Classifiers categorize referent nouns based on salient semantic features such as humanness, animacy, shape, or others (Aikhenvald and Mihas, 2019). The work described in this dissertation constitutes a thorough quantitative investigation of the distribution and function of classifiers in Mandarin Chinese and beyond. The results of the studies settle longstanding theoretical debates about the categorial nature of classifiers and contribute the first in-depth investigation of their communicative functions. Our main theoretical contribution is the development of a reliable methodology for objectively distinguishing between syntactic categories based on measurable differences in word distribution and functions. With this method, we settle the longstanding theoretical debate about the existence of multiple classifier types in Mandarin (Cheng and Sybesma, 1999; Li and Thompson, 1989; Li, 2013): we show that mensural classifiers should be distinguished from other types of classifiers based on their measurably distinctive distributions and functions. The thesis further investigates the cognitive and communicative functions of classifiers. We show that classifier-noun combinations are optimized to accommodate general communicative pressures (Blevins et al., 2017) of learning and processing, by balancing predictable and discriminative classifier-noun combinations. Previous work on noun classification implies that gender systems are inherently optimized to accommodate communicative pressures on human language learning and processing (Dye et al., 2018). The authors state that languages can make use of either grammatical (e.g., gender) or probabilistic (pre-nominal modifier sequences) noun classification to smooth the uncertainty (entropy (Shannon, 1948)) of nouns in context. We show that a genderless language like Mandarin can rely on nominal classifiers in a comparable way: classifiers appear to play the same functional role as gender markers. Noun-classifier combinations are sensitive to same frequency, similarity, and co-occurrence interactions that structure gender systems. We also present the first study of the interaction between grammatical and probabilistic noun classification. Finally, this work explores the role of classifiers in post-nominal positions in Mandarin. Previous work suggests that linguistic elements in different positions will contribute differently to human learning and processing. Based on an artificial noun learning experiment, Ramscar (2013) shows how humans will tend to rely on prefixed elements to categorize nouns while suffixed elements will help them specify noun meanings. For nouns, meaning specification is especially useful if they carry more general meanings, i.e., for nouns that occur more frequently and in a broader range of contexts. An investigation of the differences between nouns occurring with pre- vs. post-nominal classifiers confirms these findings on natural language data. We find significant differences in the frequencies of the two types of nouns: pre-nominal classifiers tend to pair with lower frequency nouns to aid prediction, while post-nominal classifiers are associated with higher frequency nouns to refine their meanings. Additionally, sortal classifiers, which emphasize the intrinsic features of nouns, are predominantly used in pre-nominal positions for categorization. In contrast, mensural classifiers, known for their flexibility in quantifying nouns, are more commonly found in post-nominal positions to aid in meaning refinement. The implications of this work extend beyond language specific findings. By elucidating the complex interplay between classifiers and communicative efficiency, our study contributes to a deeper understanding of the cognitive processes underlying noun processing and learning. It also provides a framework to further investigate the role of classifiers in other classifier languages and structures. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]