NDLI: Authorship classification: a syntactic tree mining approach

Please wait, while we are loading the content...

Mining useful patterns: my evolutionary view

Association discovery

Patterns from multiresolution 0-1 data

CloseViz: visualizing useful patterns

A framework for mining interesting pattern sets

Point-distribution algorithm for mining vector-item patterns

Margin-closed frequent sequential pattern mining

Block interaction: a generative summarization scheme for frequent patterns

Authorship classification: a syntactic tree mining approach

Pattern selection problems in multivariate time-series using equation discovery

Authorship classification: a syntactic tree mining approach

Content Provider	ACM Digital Library
Author	Weninger, Tim Kim, Hyungsul Kim, Sangkyum Han, Jiawei
Abstract	In the past, there have been dozens of studies on automatic authorship classification, and many of these studies concluded that the writing style is one of the best indicators of original authorship. From among the hundreds of features which were developed, syntactic features were best able to reflect an author's writing style. However, due to the high computational complexity of extracting and computing syntactic features, only simple variations of basic syntactic features of function words and part-of-speech tags were considered. In this paper, we propose a novel approach to mining discriminative k-embedded-edge subtree patterns from a given set of syntactic trees that reduces the computational burden of using complex syntactic structures as a feature set. This method is shown to increase the classification accuracy. We also design a new kernel based on these features. Comprehensive experiments on real datasets of news articles and movie reviews demonstrate that our approach is reliable and more accurate than previous studies.
Starting Page	65
Ending Page	73
Page Count	9
File Format	PDF
ISBN	9781450302166
DOI	10.1145/1816112.1816121
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text mining Pattern Authorship classification Discriminative pattern Closed pattern Text categorization
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in