In recent years the amount of information on the web has increased dramatically. As a
result, it has become a challenge for the researchers to find effective ways that can help us
query and extract meaning from these large repositories. Standard document search engines
try to address the problem by presenting the users a ranked list of relevant documents. In
most cases, this is not enough as the end-user has to go through the entire document to find
out the answer he is looking for. Question answering, which is the retrieving of answers
to natural language questions from a document collection, tries to remove the onus on the
end-user by providing direct access to relevant information.
This thesis is concerned with open-domain complex question answering. Unlike simple
questions, complex questions cannot be answered easily as they often require inferencing
and synthesizing information from multiple documents. Hence, we considered the task
of complex question answering as query-focused multi-document summarization. In this
thesis, to improve complex question answering we experimented with both empirical and
machine learning approaches. We extracted several features of different types (i.e. lexical,
lexical semantic, syntactic and semantic) for each of the sentences in the document
collection in order to measure its relevancy to the user query.
We have formulated the task of complex question answering using reinforcement framework,
which to our best knowledge has not been applied for this task before and has the
potential to improve itself by fine-tuning the feature weights from user feedback. We have
also used unsupervised machine learning techniques (random walk, manifold ranking) and
augmented semantic and syntactic information to improve them. Finally we experimented
with question decomposition where instead of trying to find the answer of the complex
question directly, we decomposed the complex question into a set of simple questions and
synthesized the answers to get our final result. / x, 128 leaves : ill. ; 29 cm
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:ALU.w.uleth.ca/dspace#10133/3214 |
| Date | January 2011 |
| Creators | Imam, Md. Kaisar |
| Contributors | Chali, Yllias |
| Publisher | Lethbridge, Alta. : University of Lethbridge, c2011, Arts and Science, Department of Mathematics and Computer Science |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Relation | Thesis (University of Lethbridge. Faculty of Arts and Science) |
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