Natural Language Processing meets Journalism — workshop at EMNLP 2017


EMNLP 2017 Workshop

September 7, Copenhagen, Denmark [1]

Call for Papers

With the advent of the digital era, journalism faces what seems to be a major
change in its history — data processing. While much journalistic effort has
been (and still is) dedicated to information gathering, now a great deal of
information is readily available ñ but is dispersed in a large quantity of
data. Thus processing a continuous and very large flow of data has become a
central challenge in today's journalism.

With the recognition of this challenge, it has become widely accepted that
data-driven journalism is the future. Tools which perform big data mining in
order to pick out and link together what is interesting from various multi
media resources are needed; these tools will be used as commonly as
typewriters once were. Their scope is well beyond data classification: They
need to construct sense and structure out of the never- ending flow of
reported facts, ascertaining what is important and relevant. They need to be
able to detect what is behind the text, what authors' intentions are, what
opinions are expressed and how, whose propagandistic goal an article might
serve, etc. What's more, they need to go beyond an intelligent search engine:
They need to be picky and savvy, just like good journalists, in order to help
people see what is really going on. It must be added that we have already
been subjected to a large scale invasion of seemingly new techniques: fake
news, alternative facts etc. For better or for worse, this is indeed the
reality we must make decisions in, and we must developed tools for handling
it rightly. That is, natural language processing meeting journalism is a
crucial process that has to be instantiated on each tablet , phone or monitor
on which a piece of news is displayed — for reading or writing.

At this workshop we anticipate papers that report on state-of-the-art
inquiries into the analysis and use of large to huge news corpora. A news
corpus is generally understood as scoping over newspapers, social networks,
the web, etc. The papers should present computational techniques able to
manage a huge quantity of information and/or to perform deep analyses that
extend over actual state of the art. We welcome reports on the recent
progress on overcoming the bottlenecks in open domain relation extraction,
paraphrasing, textual entailments and semantic similarity, and on their
results in analyzing news content. However, we are also greatly interested in
technologies for enhancing the communicative function of language in this
context more generally, including in computational humor, NLP creativity for
advertising, plagiarism, fake news etc.

** Topics

Advanced NLP news applications
Automatic temporal annotation
Automatic advertising and slogan generation
Causality and relatedness in news
Crowd-sourcing information gathering and reporting
Epochs and styles in journalism
Entity and event linking in social networks
Discourse similarity
Detecting patterns in developing news
Dissemination of news through social media
Fact checking on corpus extracted information
Fact Checking and Journalism Ethic
Fake News Management
Intelligent tools for journalists, publishers, news readers
Linking multi media information
News and content recommendation and personalization
New approaches to news commenting
News summarization
Analyzing and Detecting Biased Language
Trust and credibility
Tools, Platforms and Languages by/for Journalists
Opinion changing and event drifting
Patterns and cliché detection
Plagiarism detection
Political and social discourse analysis
Predicting changes in news flow
Propagandistic style detection
Providing and encouraging information diversity
Sense and discourse shifting
Social media analytics for news
Spotting important events on social networks
Story tools and narrative frameworks
Technologies for providing context to news
Trend prediction

** Organizers
Octavian Popescu, IBM Watson Research Center, US
Carlo Strapparava, FBK Research Center, Italy

** Program Committee
Enrique Alfonseca, Google, CH
Tommaso Caselli, VU University, NL
Dan Cristea, Iasi University, RO
Elena Erdmann, TU Dortmund, DE
Song Feng, IBM Research, US
Radu Gheorghiu, Uefiscdi, RO
Daniela Gifu, Iasi University, RO
Jay Hamilton, Stanford University, US
Mark Hansen, Columbia University, US
Orin Hargraves, Colorado University, US
Daisuke Kawahara, Kyoto University, JP
Kristian Kersting, TU Dortmund, DE
Shervin Malmasi, Harvard University, US
Rada Mihalcea, University of Michigan, US
Preslav Nakov, UC Berkley, US
Vivi Nastase, FBK, IT
Gozde Ozbal, FBK , IT
Mattia Rigotti, IBM Research, US
Paolo  Rosso,Universitat de Valencia, Spain
Amanda Stent, Bloomberg, US
Kristian Kersting, TU Dortmund, DE
An Vo, Xerox, Grenoble, FR
Marcos Zampieri, Saarland University, DE
Elena Zheleva, UIC, US
Torsten Zesch, Universit‰t Duisburg-Essen, DE

** Important Dates
Submission Deadline: Friday, June 2
Accepted Paper Announcement: Friday, June 30

The EMNLP official format is requested. The recommended length is 4 pages,
including references. The form of the presentation maybe oral or poster,
while in the proceedings there is no difference between the accepted papers.

The submission is anonymous. Each paper will be reviewed but at least two
independent reviewers.
Submission is made via SoftConf: [2]

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Об авторе Лидия Пивоварова

СПбГУ - старший преподаватель, University of Helsinki - PhD student
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