Mining specific features for acquiring user information needs

Abdulmohsen, Algarni & Li, Yuefeng (2013) Mining specific features for acquiring user information needs. Lecture Notes in Computer Science : Advances in Knowledge Discovery and Data Mining, 7818, pp. 532-543.

View at publisher


Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

Impact and interest:

4 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 68727
Item Type: Journal Article
Refereed: Yes
Additional Information: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I
Keywords: Feature extraction, Pattern mining, Relevance feedback, Text classification
DOI: 10.1007/978-3-642-37453-1_44
ISSN: 0302-9743
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Springer-Verlag Berlin Heidelberg
Deposited On: 19 Mar 2014 03:05
Last Modified: 18 Jan 2015 23:41

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page