About CS&CE
Prospective Students
Research
Staff
Dept Comp Sci & Comp Eng
Contact Details
La Trobe University
Victoria 3086
AUSTRALIA
Tel: +61 3 9479 1107
Fax: +61 3 9479 3060
Email: info
@cs.latrobe.edu.au
|
 |
Research - Current Postgraduates - Details
Department of Computer Science & Computer Engineering
| Abdalgader, Khaled |
|
Course:
|
PhD |
|
Research Title/Topic:
|
A Query Biased Text Mining |
|
Supervisor:
|
Dr. Andrew Skabar and Assoc. Prof. Wenny Rahayu |
Description:
There is an enormous amount of textual information available in the real world. Our research aims to investigate a text mining approach for building a query biased system that has the ability to synthesise, exploit and find useful informationor knowledge in a collection of texts. The approach will be evaluated to ensure that it is achieving its goal.
Text mining can broadly be described as the process of deriving high quality information from text, where high quality refers to some combination of relevance, novelty, and interestingness. In other words, the goals of text mining are to discover new knowledge from some text. Text mining has its origins in data mining (defined as the non-trivial extraction of implicit, previously unknown, and potentially useful knowledge from data), and is closely related to the areas of machine learning and statistical pattern recognition.
The objective of this research is to investigate a novel text mining approach that has the ability to synthesise, exploit, and find useful information/knowledge in a collection of texts, based on query-biased way.
Thus the main research question we would like to answer is: What are the capabilities to allow a query-biased text mining approach to be more robust to synthesise, exploit and find useful information/knowledge in a collection of texts? This question leads to the following sub-research questions:
- How can we measure the similarities or relationship (in content/meaning)between sentences or other entities as they appear in a collection of texts?
- What is an appropriate technique for representing text in order to allow query-biased text mining?
|
|
|
