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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Mining parliamentary data and news articlesto find patterns of collaboration between
politicians and third party actors.
Francisco Rodrıguez Drumond
DAMA & LARCA - UPC
July 7,2014
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Social Networks: a natural tool for political analysis.
Nodes: Families of thepolitical landscape of XVcentury Florence.
Links: marriages betweenfamilies (alliances).
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Analizing parliaments through SNs.
Why?
Main challenge: source of information (nodes andrelationships)
Co-sponsorship. [Fow06]Speeches. [TPL06]Strong and weak ties. [Kir11]
Can we discover relationships involving third-party actors?
Third party discoveryDefining meaningful relationships.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
An overview of our task
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
An overview of our task
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
An overview of our task
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
An overview of our task
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
SOPA: A motivating example.
Policy Networks (PN): Social networks for political analysis.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
An overview of the literature.
Co-occurrence. [EESGGHAC14], [PSIO06].
Enriching links with the strength and semantics of relations.[Tan07],[PSB07],[ZAR03].
Beyond document co-occurrence. [NCSS06],[Bra06].
A (very) related paper. [MID+13]
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
A (very) related paper.
Moschopoulos (2013) Toward the automatic extraction of policynetworks using web links and documents
Two pre-computed PNs: Ireland and Greece.
Ground truth used for measuring correlations with similaritymeasures.
Web based.
Three types of similarity metrics:
Co-occurrence metrics (Set comparisons).Text-based metrics.Link-based metrics.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Generating bill based Policy Networks: the architecture.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Finding news articles that talk about a bill.
Topic modeling:
TF-IDF for keywordextraction.
One bill - one document.Whole set of bills as thecorpus.
1,2,3-ngrams.
Top 1000 keywords for eachbill.
Querying news articles:
Bills and news articlesmodeled as vectors
Cosine similarity forcomparison.
Rocchio’s rule for improvingqueries.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Selecting relevant news articles.
Threshold: point that maximizes:
threshold = argmaxp|p − (p.b′)b′|
Intuition: point at which there isno significant gain in score.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Entity extraction and preprocessing.
MITIE for entity extraction +
1 Entity Normalization‘The Univ. lumiere Lyon 2’ → ‘Univ Lumiere Lyon 2’
2 Mapping organization initials to the whole name‘The World Life Fund (WLF) has...’→ ‘World Life Fund’ = ‘WLF’
3 Mapping partial names with full names‘George Harrison preferred .... Harrison also...’→ ‘George Harrison’ = ‘Harrison’
4 Expanding names based on the news corpus‘Politecnica de Catalunya’→ ‘Universitat Politecnica de Catalunya’
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Filtering relevant entities.
Problem: +3000 entities per bill
Noise.
Expensive comparisons.
Solution:
Document co-occurrence + Latent Semantic Indexing (LSI)for fast similarity computation.
Hierarchical Agglomerative Clustering (HAC) for groupingentities based on their similarity.
Politicians → seed entities.
Silhoutte for detecting the best cluster containing seedentities.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Computing and thresholding entity similarities.
Entities represented as vectors of 1...3-grams occurring inparagraphs they are mentioned in.
TF-IDF with sublinear TF scaling (tf = 1 + log(frequency))
Cosine similarity for comparing the vectors.
Elbows for detecting relevant entities for each entity.
Two entities e1 and e2 are related iff they are in each othersrelevant entities list.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Results.
Two bills:
BCN-World.Law of Popular Non-referendary Consults.
Look at:
Communities → colors.Influencers → node size.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
BCN-World - Organizations.
Acencas
CUP
Tripartito
Canales Y Puertos De Tarragona
Puerto De Tarragona
Diputacion De Tarragona
Camara De Comercio De Tarragona Pimec
Govern
PSC
Parlament
Ciu
Veremonte
ERC
Icv-euia
PP
La Caixa
Melco
Hard Rock
Cepta
PPC
Sociedad Centre Medics Selva Maresme
Ciutadans
Grup Peralada
Camara Catalana
AECE
URV
Value RetailFerrari
Melia
Hard Rock Cafe
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
BCN-World - Persons-Organizations.
Felip Puig
Francesc Xavier Mena
PimecDiputacion De Tarragona
Govern
Veremonte
Josep Gonzalez
Josep PobletPere Granados
Xavier Adsera
Salvador Guillermo
Isidre Faine
Xavier Pallares
Francesc Homs
Antoni Belmonte
Cepta
Dolors Llobet
Caixabank
Macia Alavedra
Javier De La Rosa
Daniel De Alfonso
Hortensia GrauJoan Herrera
Santi Vila
Jordi VilajoanaLluis Rullan
Melco
Xavier Sabate
Josep Felix Ballesteros
Puerto De Tarragona
Enrique Bañuelos
Josep Andreu
URV
Isabel Vallet
Joan Pons
Icv-euia
Pere Aragones
Parlament
Hard Rock
Grup Peralada
PSC
PP
Jordi Turull
Marta Rovira
Miquel Salazar
Jordi PonsJaume Amat
Sociedad Centre Medics Selva Maresme
Pere Navarro
Andreu Mas-colellCaixa
Damia Calvet
Oriol Junqueras
Artur Mas
Camara Catalana
Ciu
ERC
PPC
Albert Batet
Alicia Romero
Melia
Value Retail
Alejandro Fernandez
Enric MilloAlicia Sanchez-camacho
CUPEnric Genesca
Tripartito
Ciutadans
Agusti Colom
Camara De Comercio De Tarragona
Ferrari
La Roca
Ernest Maragall
Josep Mayoral
Carles Pellicer
Acencas
Francesc Perendreu
AECE
Jordi Sierra
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Law of Popular Non-referendary Consults. - Organizations.
Manresadecideix
UDC
Comunidad Valenciana
Senado
Upyd
Omnium
Tribunal Constitucional
Moviment Arenyenc
PP
Juzgado De Lo Contencioso
Parlament
GreenpeaceSolidaritat
ERC PSC
Podemos
PSOE
Icv-euia
Ciudadanos
Congreso Ciu
Compromis
CDCCUP
BNG
Barcelona DecideixReagrupament
Bildu
ANC
Els Verds
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Law of Popular Non-referendary Consults. - Persons.
Lehendakari Ibarretxe
Manuel Fraga
Carles Mora
Joan Ridao
Uriel Bertran
Felip Puig
Jordi Fabrega
Joan Saura
Miquel Calcada
Joan Puigcercos
Lluis Corominas
Jaume De Frontanya
Jose Blanco
Jose Manuel Durao BarrosoJose Luis Rodriguez Zapatero
Mariano Rajoy
Josep Maria Pelegri
Josep Lluis Carod-roviraJosep Antoni DuranJose Zaragoza
Maria Emilia Casas
Maria Mas Jose Montilla
Leon
Josep Pique
Josep Camprubi
Lauren Uria
Ramon Torramade
Jordi Pujol
Josep Rull
Pedro Sanchez
Rodrigo Rato
Javier Arenas
Jose Maria Aznar
Maria Dolores De Cospedal
Carles MartiJordi Hereu
Joan ClosXavier Trias
Jordi Portabella
Joan Ferran
Ricard Goma
Ferran Mascarell
Pasqual Maragall
Franco
Miquel Iceta
Soraya Saenz De Santamaria
Patxi Lopez
Angel Acebes
Pere Jover
Marc Carrillo
Eduardo Zaplana
Oriol Pujol
Dolors Camats
Jordi Molto
Laia Bonet
Joan Botella
Joana Ortega
Joan Tarda
Santiago Rodriguez
Joan Herrera
Ferran Requejo
Abogado Del Estado
Andreu Mumbru
Artur Mas
Assumpta Escarp
Antoni Castells
Jone Goyricelaya
Ernest Benach
Angel Ros
Francesc Homs
Jordi Ausas
Nuria De Gispert
Oriol Junqueras
Albert RiveraJordi Turull
Marta Rovira
Alicia Sanchez-camacho
Ramon Espadaler
Pere Navarro
David Fernandez
Maurici Lucena
Alfredo Perez RubalcabaCarme Forcadell
Anna Simo
Joan Ignasi ElenaJoan Rigol
Esperanza Aguirre
Jose Manuel Soria
Laia Ortiz
Carles Viver Pi-sunyer
Rosa Diez
Carme Garcia
Josep Duran Lleida
Alfred Morales
Josep Maria Alvarez
Muriel Casals
Alfred Bosch
Cayo Lara
Pedro Arriola
Jose Maria Mena
Josep Maria Terricabras
Dolors Batalla
Arnaldo Otegi
Joan Carles Gallego
Alicia Sanchez Camacho
Jose Domingo
Angel Colom
Carme Capdevila
Joan Carretero
Francesc Ribera
Ahora Marti
Ernest Maragall
Jorge Fernandez Diaz
Alfons Lopez Tena
Alfonso Alonso
Marina Llansana
Marc Sanglas
Alberto Ruiz Gallardon David Cameron
Lluis Companys
Carme Chacon
Santi Vila
Jose Antonio Perez Tapias
Jordi Guillot
Josep Felix Ballesteros
Isabel ValletAlberto Fernandez Diaz
Antonio Hernando
Cristobal Montoro
Jaume Collboni
Ban Ki-moon
Ada Colau
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Conclusions.
1 An unbiased, low-cost, automated tool to aid the process ofPolicy Network generation and analysis.
2 The system automatically:
1 Detect entities related to a bill.2 Computes and thresholds similarity measures for SN
generation.
3 The method works better for finding relationships betweenorganizations than for persons, particularly politicians.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Contributions.
1 The use of bills as a cornerstone relating political actors,allowing to:
Understand better the discovered relations.Find fine-grained relationships which would otherwise bemissed.
2 A method for combining parliamentary open data and newspapers for PN generation.
3 An unsupervised method for automatically detecting relevantentities of a given topic from a corpus of documents given aset of seed entities.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Future work.
1 A more rigorous evaluation and problem definition.
2 Improving the PN generation phase.
3 Generative models.
4 Use-case driven PN generation.
5 Time component.
6 Signed Social Network Analysis
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
The end.
Merci beacoup!Gracies!Grazie!Multumesc!
Questions?
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Understanding the representation of entities anddocuments.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
References I
Roger B Bradford, Application of latent semantic indexing ingenerating graphs of terrorist networks, Intelligence andSecurity Informatics, Springer, 2006, pp. 674–675.
Jesus Espinal-Enrıquez, J Mario Siqueiros-Garcıa, RodrigoGarcıa-Herrera, and Sergio Antonio Alcala-Corona, Aliterature-based approach to a narco-network, SocialInformatics, Springer, 2014, pp. 97–101.
James H Fowler, Connecting the congress: A study ofcosponsorship networks, Political Analysis 14 (2006), no. 4,456–487.
Justin H Kirkland, The relational determinants of legislativeoutcomes: Strong and weak ties between legislators, TheJournal of Politics 73 (2011), no. 03, 887–898.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
References II
Theodosis Moschopoulos, Elias Iosif, Leeda Demetropoulou,Alexandros Potamianos, and Shrikanth Shri Narayanan,Toward the automatic extraction of policy networks using weblinks and documents, Knowledge and Data Engineering, IEEETransactions on 25 (2013), no. 10, 2404–2417.
David Newman, Chaitanya Chemudugunta, Padhraic Smyth,and Mark Steyvers, Analyzing entities and topics in newsarticles using statistical topic models, Intelligence and SecurityInformatics, Springer, 2006, pp. 93–104.
Bruno Pouliquen, Ralf Steinberger, and Clive Best, Automaticdetection of quotations in multilingual news, Proceedings ofRecent Advances in Natural Language Processing, 2007,pp. 487–492.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
References III
Bruno Pouliquen, Ralf Steinberger, Camelia Ignat, and TamaraOellinger, arXiv preprint cs/0609066 (2006).
Hristo Tanev, Unsupervised learning of social networks from amultiple-source news corpus, MuLTISOuRcE, MuLTILINguALINfORMATION ExTRAc-TION ANd SuMMARIzATION(2007), 33.
Matt Thomas, Bo Pang, and Lillian Lee, Get out the vote:Determining support or opposition from congressionalfloor-debate transcripts, Proceedings of the 2006 conferenceon empirical methods in natural language processing,Association for Computational Linguistics, 2006, pp. 327–335.
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Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
References IV
Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella,Kernel methods for relation extraction, The Journal ofMachine Learning Research 3 (2003), 1083–1106.
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