Integrating Distributional, Compositional, and Relational Approaches to Neural Word Representations

When the field of natural language processing (NLP) entered the era of deep neural networks, the task of representing basic units of language, an inherently sparse and symbolic medium, using low-dimensional dense real-valued vectors, or embeddings, became crucial. The dominant technique to perform this task has for years been to segment input text sequences into space-delimited words, for which embeddings are trained over a large corpus by means of leveraging distributional information: a word is reducible to the set of contexts it appears in. This approach is powerful but imperfect; words not seen during the embedding learning phase, known as out-of-vocabulary words (OOVs), emerge in any plausible application where embeddings are used. One approach applied in order to combat this and other shortcomings is the incorporation of compositional information obtained from the surface form of words, enabling the representation of morphological regularities and increasing robustness to typographical errors. Another approach leverages word-sense information and relations curated in large semantic graph resources, offering a supervised signal for embedding space structure and improving representations for domain-specific rare words. In this dissertation, I offer several analyses and remedies for the OOV problem based on the utilization of character-level compositional information in multiple languages and the structure of semantic knowledge in English. In addition, I provide two novel datasets for the continued exploration of vocabulary expansion in English: one with a taxonomic emphasis on novel word formation, and the other generated by a real-world data-driven use case in the entity graph domain. Finally, recognizing the recent shift in NLP towards contextualized representations of subword tokens, I describe the form in which the OOV problem still appears in these methods, and apply an integrative compositional model to address it.Ph.D

News from Now/here: Ed Dorn, Lawrence, Kansas, & the Poetics of Migration - 1965-1970

The stylistic variety of Edward Dorn's poetic career, from the 1950s through the 1990s, has been criticized as lacking cohesion, and deemed his work's fundamental shortcoming. The earlier poetry's somber lyricism has been pitted against the caustic epigrams of the later writing, and these modes are set on either side of Gunslinger, Dorn's mock-epic of the "sicksties," which has received disproportionate scholarly attention, to the detriment of Dorn's manifold, contemporaneous work. While formal experimentation and the development of a multi-voiced perspective might provide a context for approaching Dorn's stylistic diversity, instead those objectives have been critically cemented to an embittered tendentiousness, a resistance, insufficient to address either the biography or the writing. Due to the fragmentary displacements of these assumptions, this thesis seeks an integrated reading that celebrates, rather than condemns, discrepancies in Dorn's unmoored political/poetic identity. Through unpublished archival materials, it reexamines the Gunslinger era--part of which Dorn spent among the countercultural tumult in Lawrence, Kansas--when Dorn's interest in geography expanded to address both "the landscape of the imagination," and the inevitable constraints of an ideologically-infused language