Computing and Information Systems - Theses
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ItemCrowdsourcing lexical semantic judgements from bilingual dictionary users( 2017)Words can take on many meanings, and collecting and identifying example usages representative of the full variety of meanings words can take is a bottleneck to the study of lexical semantics using statistical approaches. To perform supervised word sense disambiguation (WSD), or to evaluate knowledge-based methods, a corpus of texts annotated with senses from a dictionary may be constructed by paid experts. However, the cost usually prohibits more than a small sample of words and senses being represented in the corpus. Crowdsourcing methods promise to acquire data more cheaply, albeit with a greater challenge for quality control. Most crowdsourcing to date has incentivised participation in the form of a payment or by gamification of the resource construction task. However, with paid crowdsourcing the cost of human labour scales linearly with the output size, and while game playing volunteers may be free, gamification studies must compete with a multi-billion dollar games industry for players. In this thesis we develop and evaluate resources for computational semantics, working towards a crowdsourcing method that extracts information from naturally occurring human activities. A number of software products exist for glossing Japanese text with entries from a dictionary for English speaking students. However, the most popular ones have a tendency to either present an overwhelming amount of information containing every sense of every word or else hide too much information and risk removing senses with particular relevance to a specific text. By offering a glossing application with interactive features for exploring word senses, we create an opportunity to crowdsource human judgements about word senses and record human interaction with semantic NLP.
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ItemImproving the utility of topic models: an uncut gem does not sparkle( 2013)This thesis concerns a type of statistical model known as topic model. Topic modelling learns abstract “topics” in a collection of documents, and by “topic” we mean an idea, theme or subject. For example we may have an article that discusses space exploration, or a book about crime. Space exploration and crime, these two subjects, are the “topics” that we are talking about. As one imagine, topic modelling has a direct application in digital libraries, as it automates the learning and categorisation of topics in books and articles. The merit of topic modelling, however, is that its machinery is not limited to processing just words but symbols in general. As such, topic modelling has seen applications in other areas outside text processing such as biomedical research for inferring protein families. Most applications, however, are small scale and experimental and much of the impact is still contained in academic research. The overarching theme of the thesis is thus to improve the utility of topic modelling. We achieve this in two ways: (1) by improving a few aspects of topic modelling to make it more accessible and usable by users; and (2) by proposing novel applications of topic modelling to real-world problems. In the first step, we look into improving the preprocessing methodology of documents that serves as the creation of input for topic models. We also experiment extensively to improve the visualisation of topics—one of the main output of topic models—to increase its usability for human users. In the second step, we apply topic modelling in a lexicography-oriented work to learn and detect new meanings that have emerged in words and in the social media space to identify popular social trends. Both were novel applications and delivered promising results, demonstrating the strength and wide applicability of topic models.