Question answering using document tagging and question classification

Simple item page
dc.contributor.authorDubien, Stephen
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2007-05-13T20:30:45Z
dc.date.available2007-05-13T20:30:45Z
dc.date.issued2005
dc.degree.levelMasters
dc.descriptionviii, 139 leaves ; 29 cm.en
dc.description.abstractQuestion answering (QA) is a relatively new area of research. QA is retriecing answers to questions rather than information retrival systems (search engines), which retrieve documents. This means that question answering systems will possibly be the next generation of search engines. What is left to be done to allow QA to be the next generation of search engines? The answer is higher accuracy, which can be achieved by investigating methods of questions answering. I took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available systems to tage the documents. Our system classifies the questions using manually developed rules. I also investigated different ways which can use both these methods to answer questions and found that our methods had a comparable accuracy to some systems that use deeper processing techniques. This thesis includes investigations into modules of a question answering system and gives insights into how to go about developing a question answering system based on document tagging and question classification. I also evaluated our current system with the questions from the TREC 2004 question answering track.en
dc.identifier.urihttps://hdl.handle.net/10133/248
dc.language.isoen_USen
dc.publisherLethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2005en
dc.publisher.departmentDepartment of Mathematics and Computer Science
dc.publisher.facultyArts and Science
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en
dc.subjectDissertations, Academicen
dc.subjectQuestion-answering systemsen
dc.titleQuestion answering using document tagging and question classificationen
dc.typeThesisen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
MR17389.pdf
Size:
3.5 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.88 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Arts and Science, Faculty of
University of Lethbridge Theses