Call for Papers: Computing News Storylines 2016

Workshop in conjunction with EMNLP 2016, Austin, Texas, U.S.A


Scope and Topics

Today’s digital media ecosystem generates massive streams of news, largely in the form of individual documents (‘articles’) within which news events and narrative structures are communicated using natural language text. The increasing quantity of text documents produced by the ecosystem has presented challenges to those seeking to understand and contextualize news events and narratives over long periods of time, leading to demands for new multidimensional, multimodal and distributed representations of news events and of the narrative structures that are constructed from them. Currently, most work on cross-document temporal processing focuses on linear timelines (i.e. representations of chronologically ordered events), however not every timeline necessarily forms a good and useful storyline.

Following the success of 1st Workshop on Computing News Storylines (CNewsStory, ACL 2015), the 2nd edition of the Workshop on Computing News Storylines (CNewsStory, EMNLP 2016) aims at further exploring, investigating and understanding the cross-document connections between news events and stories.

This multidisciplinary workshop aims at gathering researchers in NLP, AI, knowledge representation and structured journalism together with journalists, policy makers and stakeholders in the news industry to discuss how NLP technology can help to deal with the current stream of information, manage the risks of information overload, identify different sources and perspectives, and provide unitary and easily intelligible representations of the larger and long-term storylines behind news articles.

We invite work on all aspects relating to the computational generation, representation, analysis or use of news storylines or their components, and on the relationships between news storylines or their components. This includes (but is not limited to) the following topics:

• Identifying and filtering relevant events
• Accumulating information from news streams
• Detecting opinions and perspectives on events
• Tracing perspective change through time
• Modelling plot structures
• Storyline stability and completeness
• Annotating storylines
• Crowdsourcing Storylines
• Temporal or causal ordering of events
• Script activation
• Big data for storylines
• Evaluation of storylines
• Discourse structure and storylines
• Visualisation of storylines
• Visualisation of news clusters
• Event factuality profiling
• Multimodal storyline generation
• Event-centred structured journalism
• Event-centered natural language generation
• Event taxonomies and ontologies
• Characteristics of journalistic events and narratives
• Representation of journalistic events and narratives
• Narrative networks
• Pattern detection in news
• Advanced NLP news applications
• Automatic Temporal Processing
• Tools for automatic fact-checking on information extracted from corpora
• News summarisation
• Pattern detection in news reports
• Trend prediction

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