Computing and Information Systems - Theses
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ItemAnalysing the interplay of location, language and links utilising geotagged Twitter content( 2018)Language use and interactions on social media are geographically biased. In this work we utilise this bias in predictive models of user geolocation and lexical dialectology. User geolocation is an important component of applications such as personalised search and recommendation systems. We propose text-based and network-based geolocation models, and compare them over benchmark datasets yielding state-of-the- art performance. We also propose hybrid and joint text and network geolocation models that improve upon text or network only models and show that the joint models are able to achieve reasonable performance in minimal supervision scenarios, as often happens in real world datasets. Finally, we also propose the use of continuous representations of location, which enables regression modelling of geolocation and lexical dialectology. We show that our proposed data-driven lexical dialectology model provides qualitative insights in studying geographical lexical variation.
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ItemSupervised algorithms for complex relation extraction( 2017)Binary relation extraction is an essential component of information extraction systems, wherein the aim is to extract meaningful relations that might exist between a pair of entities within a sentence. Binary relation extraction systems have witnessed a significant improvement over past three decades, ranging from rule-based systems to statistical natural language techniques including supervised, semi-supervised and unsupervised machine learning approaches. Modern question answering and summarization systems have motivated the need for extracting complex relations wherein the number of related entities is more than two. Complex relation extraction (CRE) systems are highly domain specific and often rely on traditional binary relation extraction techniques employed in a pipeline fashion, thus susceptible to processing-induced error propagation. In this thesis, we investigate and develop approaches to extract complex relations directly from natural language text. In particular, we deviate from the traditional disintegration of complex relations into constituent binary relations and propose usage of shortest dependency parse spanning the n related entities as an alternative to facilitate direct CRE. We investigate this proposed approach by a comprehensive study of supervised learning algorithms with a special focus on training support vector machines, convolutional neural networks and deep learning ensemble algorithms. Research in the domain of CRE is stymied by paucity of annotated data. To facilitate future exploration, we create two new datasets to evaluate our proposed CRE approaches on a pilot biographical fact extraction task. An evaluation of results on new and standard datasets concludes that usage of shortest path dependency parse in a supervised setting enables direct CRE with an improved accuracy, beating current state-of-the-art CRE systems. We further show the application of CRE to achieve state-of-the-art performance for directly extracting events without the need of disintegrating them into event trigger and event argument extraction processes.
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ItemAutomatic identification of locative expressions from informal text( 2013)Informal place descriptions that are rich in locative expressions can be found in various contexts. The ability to extract locative expressions from such informal place descriptions is at the centre of improving the quality of services, such as interpreting geographical queries and emergency calls. While much attention has been focused on the identification of formal place references (e.g., Rathmines Road) from natu- ral language, people tend to make heavy use of informal place references (e.g., my bedroom). This research addresses the problem by developing a model that is able to automatically identify locative expressions from informal text. Moreover, we study and discover insights of what aspects are helpful in the identification task. Utilising an existing manually annotated corpus, we re-annotate locative expressions and use them as the gold standard. Having the gold standard ready, we take a machine learning approach to the identification task with well-reasoned features based on observation and intuition. Further, we study the impacts of various feature setups on the performance of the model and provide analyses of experiment results. With the best performing feature setup, the model is able to achieve significant increase in performance over the baseline systems.