A New Method and Application for Studying Political Text in Multiple Languages
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The Transformation of Italian Democracy
Bulletin of Italian Politics Vol. 1, No. 1, 2009, 29-47 The Transformation of Italian Democracy Sergio Fabbrini University of Trento Abstract: The history of post-Second World War Italy may be divided into two distinct periods corresponding to two different modes of democratic functioning. During the period from 1948 to 1993 (commonly referred to as the First Republic), Italy was a consensual democracy; whereas the system (commonly referred to as the Second Republic) that emerged from the dramatic changes brought about by the end of the Cold War functions according to the logic of competitive democracy. The transformation of Italy’s political system has thus been significant. However, there remain important hurdles on the road to a coherent institutionalisation of the competitive model. The article reconstructs the transformation of Italian democracy, highlighting the socio-economic and institutional barriers that continue to obstruct a competitive outcome. Keywords: Italian politics, Models of democracy, Parliamentary government, Party system, Interest groups, Political change. Introduction As a result of the parliamentary elections of 13-14 April 2008, the Italian party system now ranks amongst the least fragmented in Europe. Only four party groups are represented in the Senate and five in the Chamber of Deputies. In comparison, in Spain there are nine party groups in the Congreso de los Diputados and six in the Senado; in France, four in the Assemblée Nationale an d six in the Sénat; and in Germany, six in the Bundestag. Admittedly, as is the case for the United Kingdom, rather fewer parties matter in those democracies in terms of the formation of governments: generally not more than two or three. -
Multi-View Learning for Hierarchical Topic Detection on Corpus of Documents
Multi-view learning for hierarchical topic detection on corpus of documents Juan Camilo Calero Espinosa Universidad Nacional de Colombia Facultad de Ingenieria, Departamento de Ingenieria de Sistemas e Industrial. Bogot´a,Colombia 2021 Multi-view learning for hierarchical topic detection on corpus of documents Juan Camilo Calero Espinosa Tesis presentada como requisito parcial para optar al t´ıtulode: Magister en Ingenier´ıade Sistemas y Computaciøn´ Director: Ph.D. Luis Fernando Ni~noV. L´ıneade Investigaci´on: Procesamiento de lenguaje natural Grupo de Investigaci´on: Laboratorio de investigaci´onen sistemas inteligentes - LISI Universidad Nacional de Colombia Facultad de Ingenieria, Departamento de Ingenieria en Sistemas e Industrial. Bogot´a,Colombia 2021 To my parents Maria Helena and Jaime. To my aunts Patricia and Rosa. To my grandmothers Lilia and Santos. Acknowledgements To Camilo Alberto Pino, as the original thesis idea was his, and for his invaluable teaching of multi-view learning. To my thesis advisor, Luis Fernando Ni~no,and the Laboratorio de investigaci´onen sistemas inteligentes - LISI, for constantly allowing me to learn new knowl- edge, and for their valuable recommendations on the thesis. V Abstract Topic detection on a large corpus of documents requires a considerable amount of com- putational resources, and the number of topics increases the burden as well. However, even a large number of topics might not be as specific as desired, or simply the topic quality starts decreasing after a certain number. To overcome these obstacles, we propose a new method- ology for hierarchical topic detection, which uses multi-view clustering to link different topic models extracted from document named entities and part of speech tags. -
Nonstationary Latent Dirichlet Allocation for Speech Recognition
Nonstationary Latent Dirichlet Allocation for Speech Recognition Chuang-Hua Chueh and Jen-Tzung Chien Department of Computer Science and Information Engineering National Cheng Kung University, Tainan, Taiwan, ROC {chchueh,chien}@chien.csie.ncku.edu.tw model was presented for document representation with time Abstract evolution. Current parameters served as the prior information Latent Dirichlet allocation (LDA) has been successful for to estimate new parameters for next time period. Furthermore, document modeling. LDA extracts the latent topics across a continuous time dynamic topic model [12] was developed by documents. Words in a document are generated by the same incorporating a Brownian motion in the dynamic topic model, topic distribution. However, in real-world documents, the and so the continuous-time topic evolution was fulfilled. usage of words in different paragraphs is varied and Sparse variational inference was used to reduce the accompanied with different writing styles. This study extends computational complexity. In [6][7], LDA was merged with the LDA and copes with the variations of topic information the hidden Markov model (HMM) as the HMM-LDA model within a document. We build the nonstationary LDA (NLDA) where the Markov states were used to discover the syntactic by incorporating a Markov chain which is used to detect the structure of a document. Each word was generated either from stylistic segments in a document. Each segment corresponds to the topic or the syntactic state. The syntactic words and a particular style in composition of a document. This NLDA content words were modeled separately. can exploit the topic information between documents as well This study also considers the superiority of HMM in as the word variations within a document. -
Large-Scale Hierarchical Topic Models
Large-Scale Hierarchical Topic Models Jay Pujara Peter Skomoroch Department of Computer Science LinkedIn Corporation University of Maryland 2029 Stierlin Ct. College Park, MD 20742 Mountain View, CA 94043 [email protected] [email protected] Abstract In the past decade, a number of advances in topic modeling have produced sophis- ticated models that are capable of generating hierarchies of topics. One challenge for these models is scalability: they are incapable of working at the massive scale of millions of documents and hundreds of thousands of terms. We address this challenge with a technique that learns a hierarchy of topics by iteratively apply- ing topic models and processing subtrees of the hierarchy in parallel. This ap- proach has a number of scalability advantages compared to existing techniques, and shows promising results in experiments assessing runtime and human evalu- ations of quality. We detail extensions to this approach that may further improve hierarchical topic modeling for large-scale applications. 1 Motivation With massive datasets and corresponding computational resources readily available, the Big Data movement aims to provide deep insights into real-world data. Realizing this goal can require new approaches to well-studied problems. Complex models that, for example, incorporate many de- pendencies between parameters have alluring results for small datasets and single machines but are difficult to adapt to the Big Data paradigm. Topic models are an interesting example of this phenomenon. In the last decade, a number of sophis- ticated techniques have been developed to model collections of text, from Latent Dirichlet Allocation (LDA)[1] through extensions using statistical machinery such as the nested Chinese Restaurant Pro- cess [2][3] and Pachinko Allocation[4]. -
The Regional Elections of 2010: Much Ado About Nothing?
Bulletin of Italian Politics Vol. 2, No. 1, 2010, 137-45 The Regional Elections of 2010: Much Ado about Nothing? Antonio Floridia Electoral Observatory of the Region of Tuscany Abstract: This article, taking its point of departure from the research presented at the annual workshop of the Italian Society for Electoral Studies, analyses the principal outcomes of the elections held in 13 Italian regions on 27 and 28 March 2010. One of the most significant features of these elections is that they do not appear to have resulted in any major changes with respect to the electoral cycle initiated in Italy by the parliamentary elections of 2008. Featuring a very low level of turnout, typical of “second-order” elections and affecting all the parties, the only winners were the parties (the Northern League and Italy of Values) which managed to consolidate their support or limit their losses. The article then analyses in more detail the result obtained by the Democratic Party and dwells on the fact that the success of the centre right, despite winning four of the regions previously governed by the centre left, does not seem, however, to have reinforced the Berlusconi government due to the growing political significance of the League and the conflicts this produces. Ultimately, the regional elections have highlighted all of the dillemmas affecting Italian politics without resolving any of them. Keywords: Berlusconi, regional elections, Lega Nord, Democratic Party As it has become accustomed to doing in the wake of a round of elections, SISE, the Italian Society for Electoral Studies (Società Italiana di Studi Elettorali), decided this year too to organise a workshop – which took place in Milan on 10 May, a few weeks after the regional elections, at the headquarters, and with the support of the Milan provincial government. -
Journal of Applied Sciences Research
Copyright © 2015, American-Eurasian Network for Scientific Information publisher JOURNAL OF APPLIED SCIENCES RESEARCH ISSN: 1819-544X EISSN: 1816-157X JOURNAL home page: http://www.aensiweb.com/JASR 2015 October; 11(19): pages 50-55. Published Online 10 November 2015. Research Article A Survey on Correlation between the Topic and Documents Based on the Pachinko Allocation Model 1Dr.C.Sundar and 2V.Sujitha 1Associate Professor, Department of Computer Science and Engineering, Christian College of Engineering and Technology, Dindigul, Tamilnadu-624619, India. 2PG Scholar, Department of Computer Science and Engineering, Christian College of Engineering and Technology, Dindigul, Tamilnadu-624619, India. Received: 23 September 2015; Accepted: 25 October 2015 © 2015 AENSI PUBLISHER All rights reserved ABSTRACT Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. In existing system, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is used.The patterns are generated from the words in the word-based topic representations of a traditional topic model such as the LDA model. This ensures that the patterns can well represent the topics because these patterns are comprised of the words which are extracted by LDA based on sample occurrence of the words in the documents. The Maximum matched pat-terns, which are the largest patterns in each equivalence class that exist in the received documents, are used to calculate the relevance words to represent topics. However, LDA does not capture correlations between topics and these not find the hidden topics in the document. To deal with the above problem the pachinko allocation model (PAM) is proposed. -
Incorporating Domain Knowledge in Latent Topic Models
INCORPORATING DOMAIN KNOWLEDGE IN LATENT TOPIC MODELS by David Michael Andrzejewski A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Sciences) at the UNIVERSITY OF WISCONSIN–MADISON 2010 c Copyright by David Michael Andrzejewski 2010 All Rights Reserved i For my parents and my Cho. ii ACKNOWLEDGMENTS Obviously none of this would have been possible without the diligent advising of Mark Craven and Xiaojin (Jerry) Zhu. Taking a bioinformatics class from Mark as an undergraduate initially got me excited about the power of statistical machine learning to extract insights from seemingly impenetrable datasets. Jerry’s enthusiasm for research and relentless pursuit of excellence were a great inspiration for me. On countless occasions, Mark and Jerry have selflessly donated their time and effort to help me develop better research skills and to improve the quality of my work. I would have been lucky to have even a single advisor as excellent as either Mark or Jerry; I have been extremely fortunate to have them both as co-advisors. My committee members have also been indispensable. Jude Shavlik has always brought an emphasis on clear communication and solid experimental technique which will hopefully stay with me for the rest of my career. Michael Newton helped me understand the modeling issues in this research from a statistical perspective. Working with prelim committee member Ben Liblit gave me the exciting opportunity to apply machine learning to a very challenging problem in the debugging work presented in Chapter 4. I also learned a lot about how other computer scientists think from meetings with Ben. -
Extração De Informação Semântica De Conteúdo Da Web 2.0
Mestrado em Engenharia Informática Dissertação Relatório Final Extração de Informação Semântica de Conteúdo da Web 2.0 Ana Rita Bento Carvalheira [email protected] Orientador: Paulo Jorge de Sousa Gomes [email protected] Data: 1 de Julho de 2014 Agradecimentos Gostaria de começar por agradecer ao Professor Paulo Gomes pelo profissionalismo e apoio incondicional, pela sincera amizade e a total disponibilidade demonstrada ao longo do ano. O seu apoio, não só foi determinante para a elaboração desta tese, como me motivou sempre a querer saber mais e ter vontade de fazer melhor. À minha Avó Maria e Avô Francisco, por sempre estarem presentes quando eu precisei, pelo carinho e afeto, bem como todo o esforço que fizeram para que nunca me faltasse nada. Espero um dia poder retribuir de alguma forma tudo aquilo que fizeram por mim. Aos meus Pais, pelos ensinamentos e valores transmitidos, por tudo o que me proporcionaram e por toda a disponibilidade e dedicação que, constantemente, me oferecem. Tudo aquilo que sou, devo-o a vocês. Ao David agradeço toda a ajuda e compreensão ao longo do ano, todo o carinho e apoio demonstrado em todas as minhas decisões e por sempre me ter encorajado a seguir os meus sonhos. Admiro-te sobretudo pela tua competência e humildade, pela transmissão de força e confiança que me dás em todos os momentos. Resumo A massiva proliferação de blogues e redes sociais fez com que o conteúdo gerado pelos utilizadores, presente em plataformas como o Twitter ou Facebook, se tornasse bastante valioso pela quantidade de informação passível de ser extraída e explorada. -
An Unsupervised Topic Segmentation Model Incorporating Word Order
An Unsupervised Topic Segmentation Model Incorporating Word Order Shoaib Jameel and Wai Lam The Chinese University of Hong Kong One line summary of the work We will see how maintaining the document structure such as paragraphs, sentences, and the word order helps improve the performance of a topic model. Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 1 Outline Motivation Related Work I Probabilistic Unigram Topic Model (LDA) I Probabilistic N-gram Topic Models I Topic Segmentation Models Overview of our model I Our N-gram Topic Segmentation model (NTSeg) Text Mining Experiments of NTSeg I Word-Topic and Segment-Topic Correlation Graph I Topic Segmentation Experiment I Document Classification Experiment I Document Likelihood Experiment Conclusions and Future Directions Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 2 Motivation Many works in the topic modeling literature assume exchangeability among the words. As a result we see many ambiguous words in topics. For example, consider few topics obtained from the NIPS collection using the Latent Dirichlet Allocation (LDA) model: Example Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 architecture order connectionist potential prior recurrent first role membrane bayesian network second binding current data module analysis structures synaptic evidence modules small distributed dendritic experts The problem with the LDA model Words in topics are not insightful. Shoaib Jameel and Wai Lam SIGIR-2013, Dublin, Ireland 3 Motivation Many works in the topic modeling literature assume exchangeability among the words. As a result we see many ambiguous words in topics. For example, consider few topics obtained from the NIPS collection using the Latent Dirichlet Allocation (LDA) model: Example Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 architecture order connectionist potential prior recurrent first role membrane bayesian network second binding current data module analysis structures synaptic evidence modules small distributed dendritic experts The problem with the LDA model Words in topics are not insightful. -
Equality, Freedom, and Democracy OUP CORRECTED AUTOPAGE PROOFS – FINAL, 16/09/20, Spi OUP CORRECTED AUTOPAGE PROOFS – FINAL, 16/09/20, Spi
OUP CORRECTED AUTOPAGE PROOFS – FINAL, 16/09/20, SPi Equality, Freedom, and Democracy OUP CORRECTED AUTOPAGE PROOFS – FINAL, 16/09/20, SPi OUP CORRECTED AUTOPAGE PROOFS – FINAL, 16/09/20, SPi Equality, Freedom, and Democracy Europe After the Great Recession By LEONARDO MORLINO with DANIELA PIANA MARIO QUARANTA FRANCESCO RANIOLO CECILIA EMMA SOTTILOTTA CLAUDIUS WAGEMANN 1 OUP CORRECTED AUTOPAGE PROOFS – FINAL, 16/09/20, SPi 1 Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Leonardo Morlino 2020. Some rights reserved. © Chapter 2 © Leonardo Morlino, Claudius Wagemann, and Francesco Raniolo 2020. Chapter 3 © Leonardo Morlino and Daniela Piana 2020. Chapter 4 © Leonardo Morlino, Mario Quaranta, and Francesco Raniolo 2020. Chapter 5 © Leonardo Morlino and Francesco Raniolo 2020. Chapter 6 © Leonardo Morlino and Daniela Piana 2020. Chapter 7 © Leonardo Morlino, Daniela Piana, and Cecilia Sottilotta 2020. The moral rights of the authors have been asserted First Edition published in 2020 Impression: 1 Some rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, for commercial purposes, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. This is an open access publication, available online and distributed under the terms of a Creative Commons Attribution – Non Commercial – No Derivatives 4.0 International licence (CC BY-NC-ND 4.0), a copy of which is available at http://creativecommons.org/licenses/by-nc-nd/4.0/. -
Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
The paper has been accepted at Engineering Applications of Artificial Intelligence on 9 May 2017. This paper can cite as: Monika Rani, Amit Kumar Dhar, O.P. Vyas, Semi-automatic terminology ontology learning based on topic modeling, Engineering Applications of Artificial Intelligence, Volume 63, August 2017, Pages 108-125,ISSN 0952- 1976,https://doi.org/10.1016/j.engappai.2017.05.006. (http://www.sciencedirect.com/science/article/pii/S0952197617300891. Semi-Automatic Terminology Ontology Learning Based on Topic Modeling Monika Rani*, Amit Kumar Dhar and O. P. Vyas Department of Information Technology, Indian Institute of Information Technology, Allahabad, India [email protected] Abstract- Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user‟s query demonstrating the effectiveness of the proposed approach. Keywords: Ontology Learning (OL), Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD), Probabilistic Latent Semantic Indexing (pLSI), MapReduce Latent Dirichlet Allocation (Mr.LDA), Correlation Topic Modeling (CTM). -
Song Lyric Topic Modeling
A Comparison of Topic Modeling Approaches for a Comprehensive Corpus of Song Lyrics Alen Lukic Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 [email protected] Abstract In the interdisciplinary field of music information retrieval (MIR), applying topic modeling techniques to corpora of song lyrics remains an open area of research. In this paper, I discuss previous approaches to this problem and approaches which remain unexplored. I attempt to improve upon previous work by harvesting a larger, more comprehensive corpus of song lyrics. I will apply previously attempted topic modeling algorithms to this corpus, including latent Dirichlet allocation (LDA). In addition, I will also apply the Pachinko allocation (PAM) tech- nique to this corpus. PAM is a directed acylic graph-based topic modeling algorithm which models correlations between topics as well as words and therefore has more expressive power than LDA. Currently, there are no documented research results which utilize the PAM technique for topic modeling in song lyrics. Finally, I will obtain a collection of human-annotated songs in order to apply a supervised topic modeling approach and use the results in order to produce one of the topic quality metrics which will be investigated in this paper. 1 Introduction The field of music information retrieval encompasses the research efforts which address several prob- lems, including genre classification, mood categorization, and topic detection in lyrics. The focus of this paper is the latter of these problems. Within the study of topic modeling, topics are defined as a set of k item I = fi1; : : : ; iN g distri- butions P1(I);:::;Pk(I).