DOCSLIB.ORG

Social Network Extraction from Text Apoorv Agarwal

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Social Network Extraction from Text Apoorv Agarwal Social Network Extraction from Text Apoorv Agarwal Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2016 c 2016 Apoorv Agarwal All Rights Reserved ABSTRACT Social Network Extraction from Text Apoorv Agarwal In the pre-digital age, when electronically stored information was non-existent, the only ways of creating representations of social networks were by hand through surveys, inter- views, and observations. In this digital age of the internet, numerous indications of social interactions and associations are available electronically in an easy to access manner as structured meta-data. This lessens our dependence on manual surveys and interviews for creating and studying social networks. However, there are sources of networks that remain untouched simply because they are not associated with any meta-data. Primary examples of such sources include the vast amounts of literary texts, news articles, content of emails, and other forms of unstructured and semi-structured texts. The main contribution of this thesis is the introduction of natural language processing and applied machine learning techniques for uncovering social networks in such sources of unstructured and semi-structured texts. Specifically, we propose three novel techniques for mining social networks from three types of texts: unstructured texts (such as literary texts), emails, and movie screenplays. For each of these types of texts, we demonstrate the utility of the extracted networks on three applications (one for each type of text). Table of Contents List of Figures. viii List of Tables. xiii I Introduction 1 1 Introduction 2 1.1 Motivation . .2 1.2 A High Level Organization of the Thesis . .5 1.3 Contributions in Terms of Techniques . .8 1.4 Contributions in Terms of Applications . 11 1.5 Thesis Organization . 13 2 A New Kind of a Social Network 14 2.1 Definition of Entities . 15 2.2 Definition of Social Events . 15 2.3 Subcategorization of Social Events . 19 2.3.1 Examples for the Subcategories of OBS . 20 2.3.2 Examples for the Subcategories of INR . 21 2.4 Social Events Considered in this Thesis . 22 2.5 Comparison of Social Events with ACE Relations and Events . 23 2.5.1 Social Events versus ACE Relations . 23 2.5.2 Social Events versus ACE Events . 24 2.6 Comparison of Social Events with Conversations . 32 2.7 Conclusion . 35 i II Extracting Social Networks from Unstructured Text 36 3 Introduction 38 3.1 Task Definition . 38 3.2 Related Work on Relation Extraction . 39 3.2.1 Bootstrapping . 40 3.2.2 Unsupervised . 42 3.2.3 Distant Supervision . 43 3.2.4 Feature Based Supervision . 44 3.2.5 Convolution Kernels Based Supervision . 46 3.2.6 Rule Based . 51 3.3 Conclusion . 52 4 Machine Learning Approach 55 4.1 Data . 56 4.1.1 Annotation Procedure . 57 4.1.2 Additional Annotation Instructions . 60 4.1.3 Inter-annotator Agreement . 61 4.2 Baselines . 65 4.2.1 Co-occurrence Based (CoOccurN)................... 65 4.2.2 Syntactic Rule Based (SynRule)..................... 66 4.2.3 Feature Based Supervision (GuoDong05)................ 69 4.2.4 Convolution Kernel Based Supervision (Nguyen09).......... 71 4.2.5 Features derived from FrameNet (BOF and SemRules)........ 74 4.3 Structures We Introduce . 76 4.3.1 Sequence on Grammatical Relation Dependency Tree (SqGRW) . 76 4.3.2 Semantic trees (FrameForest, FrameTree, FrameTreeProp) . 76 4.4 Experiments and Results: Intrinsic Evaluation . 80 4.4.1 Task Definitions . 80 4.4.2 Data Distribution . 80 4.4.3 Experimental Set-up . 81 ii 4.4.4 Experiments and Results for Social Event Detection . 81 4.4.5 Experiments and Results for Social Event Classification . 85 4.4.6 Experiments and Results for Directionality Classification . 86 4.4.7 Experiments and Results for Social Network Extraction (SNE) . 87 4.5 Conclusion and Future Work . 88 5 Application: Validating Literary Theories 93 5.1 Introduction . 93 5.2 Literary Theories . 95 5.3 Conversational, Interaction, and Observation Networks . 95 5.4 Evaluation of SINNET .............................. 97 5.4.1 Gold standards: Conv-Gold and SocEv-Gold ............ 98 5.4.2 Evaluation and Results . 99 5.4.3 Discussion of Results . 100 5.5 Expanded Set of Literary Hypotheses . 100 5.6 Methodology for Validating Hypotheses . 102 5.7 Results for Testing Literary Hypotheses . 104 5.8 Conclusion and Future Work . 106 III Extracting Networks from Emails 108 6 Introduction 111 6.1 Terminology . 111 6.2 Task Definition . 114 6.3 Literature Survey . 114 6.4 Conclusion . 116 7 Machine Learning Approach 117 7.1 Data . 117 7.1.1 A Brief History of the Enron Corporation . 117 7.1.2 The Enron Email Corpus . 120 iii 7.2 Name Disambiguation Approach . 121 7.2.1 Terminology . 121 7.2.2 Candidate Set Generation Algorithm . 122 7.2.3 Our Name Disambiguation Algorithm . 125 7.3 Experiments and Results . 125 7.3.1 Evaluation Set for Name Disambiguation . 125 7.3.2 Experiments and Results . 127 7.3.3 Examples of the Name Disambiguation Algorithm in Action . 128 7.3.4 Error Analysis . 131 7.4 Conclusion and Future Work . 132 8 Application: Predicting Organizational Dominance Relations 135 8.1 Introduction . 135 8.2 Related . 137 8.3 A New Gold Standard for Enron Hierarchy Prediction . 140 8.4 Dominance Prediction Technique . 141 8.5 Baseline Approaches . 142 8.5.1 Unsupervised SNA-Based Approach . 142 8.5.2 Supervised NLP-Based Approach . 142 8.5.3 Experiments and Results . 143 8.6 Dominance Prediction Using the Mention Network . 144 8.6.1 Set of Experiments . 144 8.6.2 Weighted versus Unweighted Networks . 146 8.6.3 Type of Network . 147 8.6.4 Linking to the Mentioned Person . 149 8.6.5 Type of Degree Centrality . 150 8.6.6 Summary: What Matters in Mentions . 150 8.7 Conclusion and Future Work . 151 iv IV Extracting Networks from Screenplays 153 9 Introduction 156 9.1 Terminology . 156 9.2 The Structure of Screenplays . 158 9.3 Task Definition . 159 9.4 Literature Survey . 160 9.5 Conclusion . 163 10 Machine Learning Approach 164 10.1 Data . 165 10.1.1 Distant Supervision . 165 10.1.2 Heuristics for Preparing Training Data . 166 10.1.3 Data Distribution . 169 10.2 Baseline Approach . 169 10.3 Machine Learning Approach . 170 10.3.1 Terminology . 171 10.3.2 Overall Machine Learning Approach . 171 10.4 Features . 174 10.5 Experiments and Results . 177 10.5.1 Training Experts . 178 10.5.2 Finding the Right Ensemble . ..
Load more

Recommended publications
  • List of Animated Films and Matched Comparisons [Posted As Supplied by Author]
    Appendix : List of animated films and matched comparisons [posted as supplied by author] Animated Film Rating Release Match 1 Rating Match 2 Rating Date Snow White and the G 1937 Saratoga ‘Passed’ Stella Dallas G Seven Dwarfs Pinocchio G 1940 The Great Dictator G The Grapes of Wrath unrated Bambi G 1942 Mrs. Miniver G Yankee Doodle Dandy G Cinderella G 1950 Sunset Blvd. unrated All About Eve PG Peter Pan G 1953 The Robe unrated From Here to Eternity PG Lady and the Tramp G 1955 Mister Roberts unrated Rebel Without a Cause PG-13 Sleeping Beauty G 1959 Imitation of Life unrated Suddenly Last Summer unrated 101 Dalmatians G 1961 West Side Story unrated King of Kings PG-13 The Jungle Book G 1967 The Graduate G Guess Who’s Coming to Dinner unrated The Little Mermaid G 1989 Driving Miss Daisy PG Parenthood PG-13 Beauty and the Beast G 1991 Fried Green Tomatoes PG-13 Sleeping with the Enemy R Aladdin G 1992 The Bodyguard R A Few Good Men R The Lion King G 1994 Forrest Gump PG-13 Pulp Fiction R Pocahontas G 1995 While You Were PG Bridges of Madison County PG-13 Sleeping The Hunchback of Notre G 1996 Jerry Maguire R A Time to Kill R Dame Hercules G 1997 Titanic PG-13 As Good as it Gets PG-13 Animated Film Rating Release Match 1 Rating Match 2 Rating Date A Bug’s Life G 1998 Patch Adams PG-13 The Truman Show PG Mulan G 1998 You’ve Got Mail PG Shakespeare in Love R The Prince of Egypt PG 1998 Stepmom PG-13 City of Angels PG-13 Tarzan G 1999 The Sixth Sense PG-13 The Green Mile R Dinosaur PG 2000 What Lies Beneath PG-13 Erin Brockovich R Monsters,
    [Show full text]
  • Distant Reading, Computational Stylistics, and Corpus Linguistics the Critical Theory of Digital Humanities for Literature Subject Librarians David D
    CHAPTER FOUR Distant Reading, Computational Stylistics, and Corpus Linguistics The Critical Theory of Digital Humanities for Literature Subject Librarians David D. Oberhelman FOR LITERATURE librarians who frequently assist or even collaborate with faculty, researchers, IT professionals, and students on digital humanities (DH) projects, understanding some of the tacit or explicit literary theoretical assumptions involved in the practice of DH can help them better serve their constituencies and also equip them to serve as a bridge between the DH community and the more traditional practitioners of literary criticism who may not fully embrace, or may even oppose, the scientific leanings of their DH colleagues.* I will therefore provide a brief overview of the theory * Literary scholars’ opposition to the use of science and technology is not new, as Helle Porsdam has observed by looking at the current debates over DH in the academy in terms of the academic debate in Great Britain in the 1950s and 1960s between the chemist and novelist C. P. Snow and the literary critic F. R. Leavis over the “two cultures” of science and the humanities (Helle Porsdam, “Too Much ‘Digital,’ Too Little ‘Humanities’? An Attempt to Explain Why Humanities Scholars Are Reluctant Converts to Digital Humanities,” Arcadia project report, 2011, DSpace@Cambridge, www.dspace.cam.ac.uk/handle/1810/244642). 54 DISTANT READING behind the technique of DH in the case of literature—the use of “distant reading” as opposed to “close reading” of literary texts as well as the use of computational linguistics, stylistics, and corpora studies—to help literature subject librarians grasp some of the implications of DH for the literary critical tradition and learn how DH practitioners approach literary texts in ways that are fundamentally different from those employed by many other critics.† Armed with this knowledge, subject librarians may be able to play a role in integrating DH into the traditional study of literature.
    [Show full text]
  • Newcastle University Eprints
    Newcastle University ePrints Knight D, Adolphs S, Carter R. CANELC: constructing an e-language corpus. Corpora 2014, 9(1), 29-56. Copyright: The definitive version of this article, published by Edinburgh University Press, 2014, is available at: http://dx.doi.org/10.3366/cor.2014.0050 Always use the definitive version when citing. Further information on publisher website: www.euppublishing.com Date deposited: 23-07-2014 Version of file: Author Accepted Manuscript This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License ePrints – Newcastle University ePrints http://eprint.ncl.ac.uk CANELC – Constructing an e-language corpus ___________________________________________________________________________ Dawn Knight1, Svenja Adolphs2 and Ronald Carter2 This paper reports on the construction of CANELC: the Cambridge and Nottingham e- language Corpus 3 . CANELC is a one million word corpus of digital communication in English, taken from online discussion boards, blogs, tweets, emails and SMS messages. The paper outlines the approaches used when planning the corpus: obtaining consent; collecting the data and compiling the corpus database. This is followed by a detailed analysis of some of the patterns of language used in the corpus. The analysis includes a discussion of the key words and phrases used as well as the common themes and semantic associations connected with the data. These discussions form the basis of an investigation of how e-language operates in both similar and different ways to spoken and written records of communication (as evidenced by the BNC - British National Corpus). Keywords: Blogs, Tweets, SMS, Discussion Boards, e-language, Corpus Linguistics 1. Introduction Communication in the digital age is a complex many faceted process involving the production and reception of linguistic stimuli across a multitude of platforms and media types (see Boyd and Heer, 2006:1).
    [Show full text]
  • RASLAN 2015 Recent Advances in Slavonic Natural Language Processing
    RASLAN 2015 Recent Advances in Slavonic Natural Language Processing A. Horák, P. Rychlý, A. Rambousek (Eds.) RASLAN 2015 Recent Advances in Slavonic Natural Language Processing Ninth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2015 Karlova Studánka, Czech Republic, December 4–6, 2015 Proceedings Tribun EU 2015 Proceedings Editors Aleš Horák Faculty of Informatics, Masaryk University Department of Information Technologies Botanická 68a CZ-602 00 Brno, Czech Republic Email: [email protected] Pavel Rychlý Faculty of Informatics, Masaryk University Department of Information Technologies Botanická 68a CZ-602 00 Brno, Czech Republic Email: [email protected] Adam Rambousek Faculty of Informatics, Masaryk University Department of Information Technologies Botanická 68a CZ-602 00 Brno, Czech Republic Email: [email protected] This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the Czech Copyright Law, in its current version, and permission for use must always be obtained from Tribun EU. Violations are liable for prosecution under the Czech Copyright Law. Editors ○c Aleš Horák, 2015; Pavel Rychlý, 2015; Adam Rambousek, 2015 Typography ○c Adam Rambousek, 2015 Cover ○c Petr Sojka, 2010 This edition ○c Tribun EU, Brno, 2015 ISBN 978-80-263-0974-1 ISSN 2336-4289 Preface This volume contains the Proceedings of the Ninth Workshop on Recent Advances in Slavonic Natural Language Processing (RASLAN 2015) held on December 4th–6th 2015 in Karlova Studánka, Sporthotel Kurzovní, Jeseníky, Czech Republic.
    [Show full text]
  • KB-Driven Information Extraction to Enable Distant Reading of Museum Collections Giovanni A
    KB-Driven Information Extraction to Enable Distant Reading of Museum Collections Giovanni A. Cignoni, Progetto HMR, [email protected] Enrico Meloni, Progetto HMR, [email protected] Short abstract (same as the one submitted in ConfTool) Distant reading is based on the possibility to count data, to graph and to map them, to visualize the re- lations which are inside the data, to enable new reading perspectives by the use of technologies. By contrast, close reading accesses information by staying at a very fine detail level. Sometimes, we are bound to close reading because technologies, while available, are not applied. Collections preserved in the museums represent a relevant part of our cultural heritage. Cataloguing is the traditional way used to document a collection. Traditionally, catalogues are lists of records: for each piece in the collection there is a record which holds information on the object. Thus, a catalogue is just a table, a very simple and flat data structure. Each collection has its own catalogue and, even if standards exists, they are often not applied. The actual availability and usability of information in collections is impaired by the need of access- ing each catalogue individually and browse it as a list of records. This can be considered a situation of mandatory close reading. The paper discusses how it will be possible to enable distant reading of collections. Our proposed solution is based on a knowledge base and on KB-driven information extraction. As a case study, we refer to a particular domain of cultural heritage: history of information science and technology (HIST).
    [Show full text]
  • IXA-Pipe) and Parsing (Alpino)
    Details from a distance? CLIN26 Amsterdam A Dutch pipeline for event detection Chantal van Son, Marieke van Erp, Antske Fokkens, Paul Huygen, Ruben Izquierdo Bevia & Piek Vossen CLOSE READING DISTANT READING NewsReader & BiographyNet Apply the detailed analyses typically associated with close (or at least non-distant) reading to large amounts of textual data ✤ NewsReader: (financial) news data ✤ Day-by-day processing of news; reconstructing a coherent story ✤ Four languages: English, Spanish, Italian, Dutch ✤ BiographyNet: biographical data ✤ Answering historical questions with digitalized data from Biography Portal of the Netherlands (www.biografischportaal.nl) using computational methods http://www.newsreader-project.eu/ http://www.biographynet.nl/ Event extraction and representation 1. Intra-document processing: Dutch NLP pipeline generating NAF files ✤ pipeline includes tokenization, parsing, SRL, NERC, etc. ✤ NLP Annotation Format (NAF) is a multi-layered annotation format for representing linguistic annotations in complex NLP architectures 2. Cross-document processing: event coreference to convert NAF to RDF representation 3. Store the NAF and RDF in a KnowledgeStore Event extraction and representation 1. Intra-document processing: Dutch NLP pipeline generating NAF files ✤ pipeline includes tokenization, parsing, SRL, NERC, etc. ✤ NLP Annotation Format (NAF) is a multi-layered annotation format for representing linguistic annotations in complex NLP architectures 2. Cross-document processing: event coreference to convert NAF to RDF 3.
    [Show full text]
  • Nick Davis Film Discussion Group December 2015
    Nick Davis Film Discussion Group December 2015 Spotlight (dir. Thomas McCarthy, 2015) On Camera Spotlight Team Robby Robinson Michael Keaton: Mr. Mom (83), Beetlejuice (88), Birdman (14) Mike Rezendes Mark Ruffalo: You Can Count on Me (00), The Kids Are All Right (10) Sacha Pfeiffer Rachel McAdams: Mean Girls (04), The Notebook (04), Southpaw (15) Matt Carroll Brian d’Arcy James: mostly Broadway: Shrek (08), Something Rotten (15) At the Globe Marty Baron Liev Schreiber: A Walk on the Moon (99), The Manchurian Candidate (04) Ben Bradlee, Jr. John Slattery: The Station Agent (03), Bluebird (13), TV’s Mad Men (07-15) The Lawyers Mitchell Garabedian Stanley Tucci: Big Night (96), The Devil Wears Prada (06), Julie & Julia (09) Eric Macleish Billy Crudup: Jesus’ Son (99), Almost Famous (00), Waking the Dead (00) Jim Sullivan Jamey Sheridan: The Ice Storm (97), Syriana (05), TV’s Homeland (11-12) The Victims Phil Saviano (SNAP) Neal Huff: The Wedding Banquet (93), TV’s Show Me a Hero (15) Joe Crowley Michael Cyril Creighton: Star and writer of web series Jack in a Box (09-12) Patrick McSorley Jimmy LeBlanc: Gone Baby Gone (07), and that’s his only other credit! Off Camera Director-Writer Tom McCarthy: See below; co-wrote Pixar’s Up (09), frequently acts Co-Screenwriter Josh Singer: writer, West Wing (05-06), producer, Law & Order: SVU (07-08) Cinematography Masanobu Takayanagi: Silver Linings Playbook (12), Black Mass (15) Original Score Howard Shore: The Lord of the Rings Trilogy (01-03), nearly 100 credits Previous features from writer-director
    [Show full text]
  • Perception of Mental Illness Based Upon Its Portrayal in Film
    University of Central Florida STARS HIM 1990-2015 2015 Perception of Mental Illness Based Upon its Portrayal in Film Erika Hanley University of Central Florida Part of the Sociology Commons Find similar works at: https://stars.library.ucf.edu/honorstheses1990-2015 University of Central Florida Libraries http://library.ucf.edu This Open Access is brought to you for free and open access by STARS. It has been accepted for inclusion in HIM 1990-2015 by an authorized administrator of STARS. For more information, please contact [email protected]. Recommended Citation Hanley, Erika, "Perception of Mental Illness Based Upon its Portrayal in Film" (2015). HIM 1990-2015. 609. https://stars.library.ucf.edu/honorstheses1990-2015/609 PERCEPTION OF MENTAL ILLNESS BASED UPON ITS PORTRAYAL IN FILM by ERIKA HANLEY A thesis submitted in partial fulfillment of the requirements for the Honors in the Major Program in Sociology in the College of Sciences and in the Burnett Honors College at the University of Central Florida Orlando, Florida Summer Term 2015 Thesis Chair: Amy Donley ABSTRACT Perceptions can be influenced by the media concerning different groups of people. As a result of the importance of the media in how individuals obtain information and formulate opinions, how different groups are presented whether negatively or positively is important. This research examines the portrayal of mental illness in films and the impact that such portrayals have on the perceptions of mental illness of the viewers. Mental illness representations can be found quite prevalently among film and the way in which it is represented can be important as to how populations perceive those with mental disorders.
    [Show full text]
  • Decoding Silver Linings Playbook
    return to updates DECODING Silver Linings Playbook by Miles Mathis First published July 6, 2014 This may be a couple of years too late, but I work on my own schedule. I can't be bothered to respond to all propaganda in a timely manner. The film Silver Linings Playbook came out in 2012 and was nominated for 8 academy awards. Jennifer Lawrence won best actress. I will now decode the movie for you. As part of that decoding, let us look at its 92% “fresh” score at Rotten Tomatoes. Along with IMDB, Rotten Tomatoes is one of the top online review sites for new movies. So a lot of people base their movie choices on the scores at these places. Like everything else, these scores are rigged. A large percentage of the “counted” reviews are written by embedded Intelligence agents, so—like the Academy Award nominations—these reviews simply act as promotion by the CIA for their own movies. You will ask for proof of that assertion, but the 92% score is proof enough by itself. Given nothing but the plot of the movie, anyone can see that score is ridiculously high. Any intelligent person can tell from the preview alone that the movie is garbage: each snippet is more cringe-inducing than the last. But the pages at Rotten Tomatoes disprove the number as well. Go to the review pages and catalog all the responses. If the movie were really so popular, this guy's thread should have been absolutely swamped by lovers of it. It isn't.
    [Show full text]
  • ISSN: 1647-0818 Lingua
    lingua Volume 12, Número 2 (2020) ISSN: 1647-0818 lingua Volume 12, N´umero 2 { 2020 Linguamatica´ ISSN: 1647{0818 Editores Alberto Sim~oes Jos´eJo~aoAlmeida Xavier G´omezGuinovart Conteúdo Artigos de Investiga¸c~ao Adapta¸c~aoLexical Autom´atica em Textos Informativos do Portugu^esBrasileiro para o Ensino Fundamental Nathan Siegle Hartmann & Sandra Maria Alu´ısio .................3 Avaliando entidades mencionadas na cole¸c~ao ELTeC-por Diana Santos, Eckhard Bick & Marcin Wlodek ................... 29 Avalia¸c~aode recursos computacionais para o portugu^es Matilde Gon¸calves, Lu´ısaCoheur, Jorge Baptista & Ana Mineiro ........ 51 Projetos, Apresentam-se! Aplicaci´onde WordNet e de word embeddings no desenvolvemento de proto- tipos para a xeraci´onautom´atica da lingua Mar´ıaJos´eDom´ınguezV´azquez ........................... 71 Editorial Ainda h´apouco public´avamosa primeira edi¸c~aoda Linguam´atica e, subitamente, eis-nos a comemorar uma d´uziade anos. A todos os que colaboraram durante todos estes anos, tenham sido leitores, autores ou revisores, a todos o nosso muito obrigado. N~ao´ef´acilmanter um projeto destes durante tantos anos, sem qualquer financi- amento. Todo este sucesso ´egra¸cas a trabalho volunt´ario de todos, o que nos permite ter artigos de qualidade publicados e acess´ıveisgratuitamente a toda a comunidade cient´ıfica. Nestes doze anos muitos foram os que nos acompanharam, nomeadamente na comiss~aocient´ıfica. Alguns dos primeiros membros, convidados em 2008, continuam ativamente a participar neste projeto, realizando revis~oesapuradas. Outros, por via do seu percurso acad´emico e pessoal, j´an~aocolaboram connosco. Como sinal de agradecimento temos mantido os seus nomes na publica¸c~ao.
    [Show full text]
  • The Stars Are Aligning in Toronto
    The Stars are Aligning in Toronto The Toronto International Film Festival (TIFF), now in its 40th year, has grown from an upstart film movement in 1976 to a global cinematic tour de force. Whereas Cannes is known as the Queen of Film Festivals, the Toronto International Film Festival has risen to second in the rankings, frequently referred to as Cannes’ Feisty Princess. For this year’s opening, Quebec filmmaker Jean-Marc Vallée landed TIFF’s coveted opening gala slot withDemolition , starring Jake Gyllenhaal, Naomi Watts and Chris Cooper. Jean- Marc Vallée, of course, also directed Matthew McConaughey in Dallas Buyers Club (a TIFF premiere in 2013) and Reese Witherspoon in Wild. TIFF’s long history is rich with under-the-radar movies that went on to become international blockbusters: Chariots of Fire (1981), The Big Chill (1983), The Fisher King (1991), American Beauty (1999), Crash (2004), Slumdog Millionaire (2009), The King’s Speech (2010) and Silver Linings Playbook (2012). Here are our top picks from this year’s festival: Black Mass Starring Johnny Depp as Boston’s feared underworld mobster turned FBI informant, James “Whitey” Bolger, is being heralded as the breakout role in Depp’s current career slump and an Oscar-worthy performance. Depp is evil incarnate right down to his creepy blue contact lenses. Kevin Bacon, Benedict Cumberbatch and Dakota Johnson co-star. In theaters now. The Martian Director Ridley Scott and Matt Damon team up to knock this space film out of the park. Damon is an astronaut stranded on Mars. An out-of-this-world MacGiver, he relies on his expansive engineering and botany skills to stay alive as the ground crew figures out how to get him home.
    [Show full text]
  • 89Th Annual Academy Awards® Oscar® Nominations Fact
    ® 89TH ANNUAL ACADEMY AWARDS ® OSCAR NOMINATIONS FACT SHEET Best Motion Picture of the Year: Arrival (Paramount) - Shawn Levy, Dan Levine, Aaron Ryder and David Linde, producers - This is the first nomination for all four. Fences (Paramount) - Scott Rudin, Denzel Washington and Todd Black, producers - This is the eighth nomination for Scott Rudin, who won for No Country for Old Men (2007). His other Best Picture nominations were for The Hours (2002), The Social Network (2010), True Grit (2010), Extremely Loud & Incredibly Close (2011), Captain Phillips (2013) and The Grand Budapest Hotel (2014). This is the first nomination in this category for both Denzel Washington and Todd Black. Hacksaw Ridge (Summit Entertainment) - Bill Mechanic and David Permut, producers - This is the first nomination for both. Hell or High Water (CBS Films and Lionsgate) - Carla Hacken and Julie Yorn, producers - This is the first nomination for both. Hidden Figures (20th Century Fox) - Donna Gigliotti, Peter Chernin, Jenno Topping, Pharrell Williams and Theodore Melfi, producers - This is the fourth nomination in this category for Donna Gigliotti, who won for Shakespeare in Love (1998). Her other Best Picture nominations were for The Reader (2008) and Silver Linings Playbook (2012). This is the first nomination in this category for Peter Chernin, Jenno Topping, Pharrell Williams and Theodore Melfi. La La Land (Summit Entertainment) - Fred Berger, Jordan Horowitz and Marc Platt, producers - This is the first nomination for both Fred Berger and Jordan Horowitz. This is the second nomination in this category for Marc Platt. He was nominated last year for Bridge of Spies. Lion (The Weinstein Company) - Emile Sherman, Iain Canning and Angie Fielder, producers - This is the second nomination in this category for both Emile Sherman and Iain Canning, who won for The King's Speech (2010).
    [Show full text]