Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.

Analysis, optimization and development of an answer scoring system

The main contribution of this work is to analyze and describe the state of the art performance as regards answer scoring systems from the SemEval- 2013 task, as well as to continue with the development of an answer scoring system (EHU-ALM) developed in the University of the Basque Country. On the overall this master thesis focuses on finding any possible configuration that lets improve the results in the SemEval dataset by using attribute engineering techniques in order to find optimal feature subsets, along with trying different hierarchical configurations in order to analyze its performance against the traditional one versus all approach. Altogether, throughout the work we propose two alternative strategies: on the one hand, to improve the EHU-ALM system without changing the architecture, and, on the other hand, to improve the system adapting it to an hierarchical con- figuration. To build such new models we describe and use distinct attribute engineering, data preprocessing, and machine learning techniques.

The future of energy: are competitive markets and nuclear power the answer?

The inaugural lecture of Professor Stephen Thomas at the University of Greenwich, 4th February 2010. It examines whether further pursuit of competition in energy markets and expansion in the role of nuclear power can be the main elements in a policy to meet goals of security, sustainability and affordability.

The varieties of student experience - an open research question and some ways to answer it

Based on meetings of the Society for Research into Higher Education’s Student Experience Network over the past three years, the genuinely open research question is posed whether there is one or more undergraduate student experience within English higher education. Answering this question depends on whether what is taught or what is learnt is examined. If the latter, then a unitary student experience can be said to exist only in the narrowest of normative senses. What undergraduates actually learn – defined in the widest sense – is the $64,000 question of research on the student experience. Various ways to answer this question are proposed, including using students to research students. Conceptual tools to apply to findings can be developed from youth studies and cognate disciplines, particularly in relation to student identities and aspirations. Lastly, these proposals are placed in the wider context of comparative models of the varieties of student experience, including those emerging in the UK’s national regions.

Is the multiracial congregation an answer to the problem of race? Comparative perspectives from South Africa and the USAF

This paper uses a comparative perspective to analyze how multiracial congregations may contribute to racial reconciliation in South Africa. Drawing on the large-scale study of multiracial congregations in the USA by Emerson et al., it examines how they help transform antagonistic identities and make religious contributions to wider reconciliation processes. It compares the American research to an ethnographic study of a congregation in Cape Town, identifying cross-national patterns and South African distinctives, such as discourses about restitution, AIDS, inequality and women. The extent that multiracial congregations can contribute to reconciliation in South Africa is linked to the content of their worship and discourses, but especially to their ability to dismantle racially aligned power structures. © Koninklijke Brill NV, 2008.