Answer extraction for simple and complex questions
| dc.contributor.author | Joty, Shafiq Rayhan | |
| dc.contributor.author | University of Lethbridge. Faculty of Arts and Science | |
| dc.contributor.supervisor | Chali, Yllias | |
| dc.date.accessioned | 2008-09-25T20:40:58Z | |
| dc.date.available | 2008-09-25T20:40:58Z | |
| dc.date.issued | 2008 | |
| dc.degree.level | Masters | |
| dc.description | xi, 214 leaves : ill. (some col.) ; 29 cm. -- | en |
| dc.description.abstract | When a user is served with a ranked list of relevant documents by the standard document search engines, his search task is usually not over. He has to go through the entire document contents to find the precise piece of information he was 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 question answering. We have considered both simple and complex questions. Simple questions (i.e. factoid and list) are easier to answer than questions that have complex information needs and require inferencing and synthesizing information from multiple documents. Our question answering system for simple questions is based on question classification and document tagging. Question classification extracts useful information (i.e. answer type) about how to answer the question and document tagging extracts useful information from the documents, which is used in finding the answer to the question. For complex questions, 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. One hill climbing local search strategy is used to fine-tune the feature-weights. We also experimented with two unsupervised machine learning techniques: k-means and Expectation Maximization (EM) algorithms and evaluated their performance. For all these methods, we have shown the effects of different kinds of features. | en |
| dc.identifier.uri | https://hdl.handle.net/10133/666 | |
| dc.language.iso | en_US | en |
| dc.publisher | Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2008 | en |
| dc.publisher.department | Department of Mathematics and Computer Science | en |
| dc.publisher.faculty | Arts and Science | en |
| dc.relation.ispartofseries | Thesis (University of Lethbridge. Faculty of Arts and Science) | en |
| dc.subject | Dissertations, Academic | en |
| dc.subject | Natural language processing (Computer science) | en |
| dc.subject | Keyword searching | en |
| dc.title | Answer extraction for simple and complex questions | en |
| dc.type | Thesis | en |