Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/43661
Title: Data mining for mathematical question answering community
Authors: Ma, Kai
Keywords: DRNTU::Engineering::Computer science and engineering::Information systems::Database management
Issue Date: 2011
Abstract: Question Answering (QA) communities such as Yahoo! Answers and Baidu Zhidao are currently very popular with millions of users. The QA communities are particularly useful for the educational domain such as mathematics. Similar to traditional QA communities, a mathematical QA community should also allow users to search, ask, answer and discover mathematical questions. However, as mathematical formulas are highly symbolic and structured, it is challenging to develop such a mathematical QA community. In this research, we aim to propose efficient and effective techniques for supporting the ”search, ask, answer and discover” framework for a mathematical QA community. In particular, we focus on investigating different data mining techniques for mathematical question search, mathematical question topic classification and human expert finding. Mathematical question search will help retrieve a set of similar mathematical problems together with the answers posted by other users. Mathematical question topic classification will help recommend the possible topics of user posted questions. Human expert finding will help find a list of experts who are most likely able to answer a posted question according to their expertise.
URI: http://hdl.handle.net/10356/43661
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
Thesis.pdf
  Restricted Access
2.18 MBAdobe PDFView/Open

Page view(s) 50

267
Updated on Dec 1, 2020

Download(s)

5
Updated on Dec 1, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.