Natural Language Processing for Slang

Slang is a common type of language that makes creative and highly flexible use of words. A basic problem that language users tackle is how to develop and interpret novel slang terms for communication in a community. This problem is relevant for natural language processing since new slang expressions often emerge in daily conversations and online social media. However, principled computational approaches to modeling slang are lacking, which presents key challenges to the effective natural language processing of slang. In this dissertation, I develop a computational framework that offers new methodologies for the automated generation, interpretation, and translation of English slang word usages, as well as for characterizing the principles in slang variation across language communities. My dissertation is organized into three main parts. The first part addresses the under-explored problem of slang semantic extension, namely how existing words in the lexicon take on new meanings in informal context. I develop a generative framework that combines contrastive learning with probabilistic models of semantic chaining to capture slang semantic extension. By leveraging dictionary-based resources of slang, I show how the learned semantic representations more accurately predict slang word choices compared to existing approaches that rely more exclusively on corpus data. The second part of my dissertation tackles the inverse problem of slang interpretation by applying these semantic representations to interpret and translate novel slang usages in natural text. I show how this approach provides better accuracy and sample efficiency in both slang interpretation and translation, in comparison to baseline contextualized language models. Finally, the third part of my dissertation investigates semantic variation of slang across different language communities focusing on a comparative study of US and UK. I show that models incorporating either communicative need or semantic chaining can predict the regional identity of slang usages. In summary, my dissertation contributes a principled framework for modeling the lexical semantics and usages of English slang, and it opens up future opportunities for the computational investigation and automated processing of informal language across a diverse set of communities and languages.

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