Computing and Information Systems - Theses

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Subject: machine learning × Subject: natural language processing × End date: 2013 ×

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    Automatic identification of locative expressions from informal text
    Liu, Fei ( 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.
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    The effects of sampling and semantic categories on large-scale supervised relation extraction
    Willy ( 2012)
    The purpose of relation extraction is to identify novel pairs of entities which are related by a pre-specified relation such as hypernym or synonym. The traditional approach to relation extraction is to building a dedicated system for a particular relation, meaning that significant effort is required to repurpose the approach to new relations. We propose a generic approach based on supervised learning, which provides a standardised process for performing relation extraction on different relations and domains. We explore the feasibility of the approach over a range of relations and corpora, focusing particularly on the development of a realistic evaluation methodology for relation extraction. In addition to this, we investigate the impact of semantic categories on extraction effectiveness.