Current Question Answering (QA) systems have been significantly advanced in demonstrating
finer abilities to answer simple factoid and list questions. Such questions are easier
to process as they require small snippets of texts as the answers. However, there is
a category of questions that represents a more complex information need, which cannot
be satisfied easily by simply extracting a single entity or a single sentence. For example,
the question: “How was Japan affected by the earthquake?” suggests that the inquirer is
looking for information in the context of a wider perspective. We call these “complex questions”
and focus on the task of answering them with the intention to minimize the existing
gaps in the literature.
The major limitation of the available search and QA systems is that they lack a way of
measuring whether a user is satisfied with the information provided. This was our motivation
to propose a reinforcement learning formulation to the complex question answering
problem. Next, we presented an integer linear programming formulation where sentence
compression models were applied for the query-focused multi-document summarization
task in order to investigate if sentence compression improves the overall performance.
Both compression and summarization were considered as global optimization problems.
We also investigated the impact of syntactic and semantic information in a graph-based
random walk method for answering complex questions. Decomposing a complex question
into a series of simple questions and then reusing the techniques developed for answering
simple questions is an effective means of answering complex questions. We proposed a
supervised approach for automatically learning good decompositions of complex questions
in this work. A complex question often asks about a topic of user’s interest. Therefore, the
problem of complex question decomposition closely relates to the problem of topic to question
generation. We addressed this challenge and proposed a topic to question generation
approach to enhance the scope of our problem domain. / xi, 192 leaves : ill. ; 29 cm
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:ALU.w.uleth.ca/dspace#10133/3436 |
| Date | January 2013 |
| Creators | Hasan, Sheikh Sadid Al |
| Contributors | Chali, Yllias |
| Publisher | Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, 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_CA |
| Detected Language | English |
| Type | Thesis |
| Relation | Thesis (University of Lethbridge. Faculty of Arts and Science) |
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