Dissertation Information for Min SongNAME:
DEGREE:
DISCIPLINE:
SCHOOL: ADVISORS: COMMITTEE MEMBERS: MPACT Status: Fully Complete Title: Robust knowledge extraction over large text collections Abstract: Automatic knowledge extraction over large text collections has been a challenging task due to many constraints such as needs of large annotated training data, requirement of extensive manual processing of data, and huge amount of domain-specific terms. In order to address these constraints, this study proposes and develops a complete solution for extracting knowledge from large text collections with minimum human intervention. As a testbed system, a novel robust and quality knowledge extraction system, called RIKE, has been developed. The following three research questions are examined to evaluate RIKE: (1) How accurately does RIKE retrieve the promising documents for information extraction from huge text collections such as MEDLINE or TREC? (2) Does ontology enhance extraction accuracy of RIKE in retrieving the promising documents? (3) How well does RIKE extract the target entities from a huge medical text collection, MEDLINE? |
MPACT Scores for Min SongA = 0 Advisors and Advisees Graphgenerating graph, please reload |
Students under Min Song
ADVISEES:
- None
COMMITTEESHIPS:
- Xuning Tang - Drexel University (2013)