Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)

This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory 2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2015) at the China National Convention Center in Beijing, on July 31st 2015. Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study od storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The workshop aimed at bringing together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future. They form a valuable potential for exploiting news data in an innovative way.JRC.G.2-Global security and crisis managemen

A Survey on Event-based News Narrative Extraction

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.Comment: 37 pages, 3 figures, to be published in the journal ACM CSU

Brat2Viz: a tool and pipeline for visualizing narratives from annotated texts

Narrative Extraction from text is a complex task that starts by identifying a set of narrative elements (actors, events, times), and the semantic links between them (temporal, referential, semantic roles). The outcome is a structure or set of structures which can then be represented graphically, thus opening room for further and alternative exploration of the plot. Such visualization can also be useful during the on-going annotation process. Manual annotation of narratives can be a complex effort and the possibility offered by the Brat annotation tool of annotating directly on the text does not seem suciently helpful. In this paper, we propose Brat2Viz, a tool and a pipeline that displays visualization of narrative information annotated in Brat. Brat2Viz reads the annotation file of Brat, produces an intermediate representation in the declarative language DRS (Discourse Representation Structure), and from this obtains the visualization. Currently, we make available two visualization schemes: MSC (Message Sequence Chart) and Knowledge Graphs. The modularity of the pipeline enables the future extension to new annotation sources, different annotation schemes, and alternative visualizations or representations. We illustrate the pipeline using examples from an European Portuguese news corpus

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval