Article | Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania | Automatic Thematic Classification of the Titles of the Seimas Votes
Göm menyn

Title:
Automatic Thematic Classification of the Titles of the Seimas Votes
Author:
Vytautas Mickevičius: Vytautas Magnus University / Baltic Institute of Advanced Technology, Kaunas, Lithuania Tomas Krilavičius: Vytautas Magnus University / Baltic Institute of Advanced Technology, Kaunas, Lithuania Vaidas Morkevičius: Kaunas University of Technology, Institute of Public Policy and Administration, Lithuania Aušra Mackuté-Varoneckienė: Vytautas Magnus University, Kaunas, Lithuania
Download:
Full text (pdf)
Year:
2015
Conference:
Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Issue:
109
Article no.:
028
Pages:
225-231
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2015-05-06
ISBN:
978-91-7519-098-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

Statistical analysis of parliamentary roll-call votes is an important topic in political science as it reveals ideological positions of members of parliament and factions. However, these positions depend on the issues debated and voted upon as well as on attitude towards the governing coalition. Therefore, analysis of carefully selected sets of roll-call votes provides deeper knowledge about members of parliament behavior. However, in order to classify roll-call votes according to their topic automatic text classifiers have to be employed, as these votes are counted in thousands. In this paper we present results of an ongoing research on thematic classification of roll-call votes of the Lithuanian Parliament. Also, this paper is a part of a larger project aiming to develop the infrastructure designed for monitoring and analyzing roll-call voting in the Lithuanian Parliament.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Vytautas Mickevičius, Tomas Krilavičius, Vaidas Morkevičius, Aušra Mackuté-Varoneckienė
Title:
Automatic Thematic Classification of the Titles of the Seimas Votes
References:

M.A. Bailey. 2007. Comparable Preference Estimates across Time and Institutions for the Court, Congress, and Presidency. American Jrnl. of Political Science, 51(3):433–448.

B.S. Harish, D.S. Guru, and S. Manjunath. 2010. Representation and Classification of Text Documents: a Brief Review. IJCA,Special Issue on RTIPPR, (2):110–119.

S. Hix, A. Noury, and G. Roland. 2006. Dimensions of Politics in the European Parliament. American Jrnl. of Political Science, 50(2):494–520.

A. Hotho, A. N¨urnberger, and G. Paaß. 2005. A Brief Survey of Text Mining. Jrnl for Comp. Linguistics and Language Technology, 20:19–62.

S. Jackman. 2001. Multidimensional Analysis of Roll Call. Political Analysis, 9(3):227–241.

A. Jakulin, W. Buntine, T.M. La Pira, and H. Brasher. 2009. Analyzing the U.S. Senate in 2003: Similarities, Clusters and Blocs. Political Analysis, 17:291–310.

T. Joachims. 1998. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proc. of ECML-98, 10th European Conf. on Machine Learning, pages 137–142, DE.

J. Kapociute-Dzikiene and A. Krupavicius. 2014. Predicting Party Group from the Lithuanian Parliamentary Speeches. ITC, 43(3):321–332.

J. Kapociute-Dzikiene, F. Vaasen, A Krupavicius, and W. Daelemens. 2012. Improving Topic Classification for Highly Inflective Languages. In Proc. of COLING 2012, pages 1393–1410.

T. Krilavicius and V. Morkevicius. 2011. Mining Social Science Data: a Study of Voting of Members of the Seimas of Lithuania Using Multidimensional Scaling and Homogeneity Analysis. Intelektin ?e ekonomika, 5(2):224–243.

T. Krilavi?cius and V. Morkevicius. 2013. Voting in Lithuanian Parliament: is there Anything More than Position vs. Opposition? In Proc. of 7th General Conf. of the ECPR Sciences Po Bordeaux.

T. Krilavicius and A. Žilinskas. 2008. On Structural Analysis of Parlamentarian Voting Data. Informatica, 19(3):377–390.

M.S. Lynch and A.J. Madonna. 2012. Viva Voce: Implications from the Disappearing Voice Vote, 1865-1996. Social Science Quarterly, 94:530–550.

V. Mickevicius, T. Krilavicius, and V. Morkevicius. 2014. Analysing Voting Behavior of the Lithuanian Parliament Using Cluster Analysis and Multidimensional Scaling: Technical Aspects. In Proc. of the 9th Int. Conf. on Electrical and Control Technologies (ECT), pages 84–89.

K.T. Poole. 2005. Spatial Models of Parliamentary Voting. Cambridge Univ. Press.

J.M. Roberts, S.S. Smith, and S.R. Haptonstahl. 2009. The Dimensionality of Congressional Voting Reconsidered.

J. Shawe-Taylor and N. Cristianini. 2004. Kernel Methods for Pattern Analysis. Cambridge University Press.

R Core Team, 2013. R: A Language and Environment for Statistical Computing. R Found. for Stat. Comp., Vienna, Austria.

R. Užupyte and V. Morkevicius. 2013. Lietuvos Respublikos Seimo Nariu¸ Balsavimu¸ Tyrimas Pasitelkiant Socialiniu¸ Tinklu¸ Analize¸: Tinklo Konstravimo Metodologiniai Aspektai. In Proc. of the 18th Int. Conf. Information Society and University Studies, pages 170–175.

V. Vapnik and C. Cortes. 1995. Support-Vector Networks. Machine Learning, 2:273–297.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Vytautas Mickevičius, Tomas Krilavičius, Vaidas Morkevičius, Aušra Mackuté-Varoneckienė
Title:
Automatic Thematic Classification of the Titles of the Seimas Votes
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2017-02-21