Title :
A Topic-based Document Retrieval Framework
Author :
Jia, Xiping ; Ma, Zhenyuan
Author_Institution :
Sch. of Comput. Sci., Guangdong Polytech. Normal Univ., Guangzhou, China
Abstract :
A Topic-based Document Retrieval Framework (TDRF) is proposed in this paper to resolve the topic-based document retrieval. The TDRF includes nine parts, of which Corpus Topic Learning, Query Topic Learning and Relationship Sorting are the core. Experiments on similar document retrieval showed that TDRF´s instance outperforms the Vector Space Model (VSM) in average precision, recall and f-measure. The value of TDRF may lie in that it provides a simple, universal and novel methodology for document retrieval.
Keywords :
document handling; information retrieval; learning (artificial intelligence); TDRF; VSM; average precision; corpus topic learning; query topic learning; relationship sorting; topic based document retrieval framework; vector space model; Computational modeling; Correlation; Educational institutions; Indexing; Information retrieval; Sorting; Vectors; NLP; document retrieval; information retrieval; topic learning;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
DOI :
10.1109/FSKD.2012.6234372
