Browsing by Author "King, Milton"

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    Determining if this word is used like that word: predicting usage similarity with supervised and unsupervised approaches
    (University of New Brunswick, 2017) King, Milton; Cook, Paul
    Determining the meaning of a word in context is an important task for a variety of natural language processing applications such as translating between languages, summarizing paragraphs, and phrase completion. Usage similarity (USim) is an approach to describe the meaning of a word in context that does not rely on a sense inventory -- a set of dictionary-like definitions. Instead, pairs of usages of a target word are rated in terms of their similarity on a scale. In this thesis, we evaluate unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that they can increase the performance of our models. We look into a more detailed evaluation, observing the performance on different parts-of-speech as well as the change in performance when using different features. Our models also do competitively well in two word sense induction tasks, which involve clustering instances of a word based on the meaning of the word in context.
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    That ain’t how I speak: Personalizing natural language processing
    (University of New Brunswick, 2021-10) King, Milton; Cook, Paul
    Natural language processing (NLP) involves automatically analyzing text written by human authors. People develop their own use of a language known as an idiolect, which could result in poor performance from generic NLP systems. Ideally, each person would have their own personalized system that is tailored toward them. In this thesis, I demonstrate the potential benefits of personalizing systems in three different NLP tasks, which include language modeling (estimating the probability of a sequence of words), authorship verification (determining if a document belongs to a specific person), and word sense disambiguation (assigning a dictionary-like meaning to a word in context). Personalization in these topics has not been widely studied and to the best of my knowledge, this is the first work to consider personalization with word sense disambiguation, for which I design a novel dataset. For each task, I show the increase in performance that the proposed personalized models have against state-of-the-art models. The experiments in this thesis are designed without consideration of people’s demographic and all personalized methods require relatively low amounts of text from an individual. These two criteria are respected to ensure the personalized methods work well for each individual regardless of their demographic or the amount of text they have authored.