NDLI: A Multi-topic Meta-classification Scheme for Analyzing Lobbying Disclosure Data

Content Provider	IEEE Xplore Digital Library
Author	Liao, X.L. Chengcui Zhang Smith, A.D. Savage, G.T.
Copyright Year	2015
Description	Author affiliation: COLLAT Sch. of Bus., Univ. of Alabama at Birmingham, Birmingham, AL, USA (Smith, A.D.; Savage, G.T.) \|\| Dept. Comput. & Inf. Sci., Univ. of Alabama at Birmingham, Birmingham, AL, USA (Liao, X.L.; Chengcui Zhang)
Abstract	For the functioning of American democracy, the Lobbying Disclosure Act (LDA), for the very first time, provides data to empirically research interest groups behaviors and their influence on congressional policymaking. One of the main research challenges is to automatically find the topic(s), by short & sparse text classification, in a large corpus of unorganized, semi-structured, and poorly connected lobbying filings to reveal the underlying purpose(s) of these lobbying activities. Common techniques for alleviating data sparseness are to enrich the context of data by external information. This paper, however, proposed an inter-disciplinary yet practical solution to this problem using a Multi-Topic Meta-Classification (MTMC) scheme built upon a set of semantic attributes (i.e., General Issue, Specific Issue, and Bill Info.), integrated with a domain-specific Policy Agenda (PA) coding/labeling procedure. First, multi-label base-classifiers that have been transformed into multi-class classification problems were learned from the abovementioned three semantic sources, respectively, second, to render reliability classification, one meta-classifier per attribute was trained based on meta-instances dataset labeled in a cross-validation fashion, third, the final prediction is made via fusing the reliable outputs of such ensembles of classifiers. Experiments demonstrated satisfactory classification performance with various evaluation measures on such a real-world textual dataset that poses many challenges including problems with noisy data and semantic ambiguity.
Starting Page	349
Ending Page	356
File Size	1115039
Page Count	8
File Format	PDF
ISBN	9781467366564
DOI	10.1109/IRI.2015.60
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Reliability Encoding Context Training Kernel Accuracy information fusion machine learning applications multi-class & multi-label classification meta-classifier
Content Type	Text
Resource Type	Article

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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
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