Topy: Real-Time Story Tracking via Social Tags

Poghosyan, Gevorg; Qureshi, Muhammad Atif; Ifrim, Georgiana

doi:10.1007/978-3-319-46131-1_10

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…The live system used in the experiments for evaluation is described in our previous work (Poghosyan et al, 2016) and is available online 19 .…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

Caselli

Miller²,

Erp³

et al. 2016

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at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study of 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 aims to bring together researchers from different communities working on representing and extracting narrative structures in news, a text genre popular in NLP research but which has received little attention in research into narrative structure, representation and analysis.Current advances in NLP technology have made it possible 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 and information specialists 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 society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organisations) or events (e.g. the 2016 presidential elections). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections for the future. They constitute a huge potential for exploiting news data in an innovative way.Of the 14 submissions we received, 8 were accepted that touch upon different aspects of narrative research in news. Three contributions describe approaches to detect storylines either from news (Brüggermann et al.), from Tweets (Krishnan and Eisenstein), or from news but with metadata added via Twitter (Poghosyan and Ifrim). Besides detecting storylines, different aspects of storylines such as diegesis and point of view are also addressed (Eisenberg and Finlayson). The second topic that is addressed is annotation and representation of storylines (Caselli and Vossen and O'Gorman et al.). Related to this is the analysis of the distribution of narrative schemas in a corpus, which may help further the discussion on corpus creation (Simonson and Davis). Finally, ideas on how to put storylines to use in a newsroom are discussed in Caswel...

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“…The live system used in the experiments for evaluation is described in our previous work (Poghosyan et al, 2016) and is available online 19 .…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

Caselli

Miller²,

Erp³

et al. 2016

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“…At present, governments are opting for limited use of dashboards when it comes to smart cities, and smart governance [262] to share insight and stats. In this regard, there were some applications developed to bring insights during the election campaigns [263] and movements of public concerns and social causes [264] (e.g., austerity, Brexit, refugee crisis) with the use of AI. These types of dashboards bring some transparency, such as which politician or political party has a specific stance concerning a domestic political issue on their social media, what is the public voice concerning austerity, and the voice of different media outlets.…”

Section: ) How Xai Can Helpmentioning

confidence: 99%

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

Wang,

Qureshi,

Miralles-Pechuán

et al. 2024

IEEE Open J. Commun. Soc.

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Around 2020, 5G began its commercialization journey, and discussions about the nextgeneration networks (such as 6G) emerged. Researchers predict that 6G networks will have higher bandwidth, coverage, reliability, energy efficiency, and lower latency, and will be an integrated "humancentric" network system powered by artificial intelligence (AI). This 6G network will lead to many real-time automated decisions, ranging from network resource allocation to collision avoidance for self-driving cars. However, there is a risk of losing control over decision-making due to the high-speed, data-intensive AI decision-making that may go beyond designers' and users' comprehension. To mitigate this risk, explainable AI (XAI) methods can be used to enhance the transparency of the black-box AI decisionmaking process. This paper surveys the application of XAI towards the upcoming 6G age, including 6G technologies (such as intelligent radio and zero-touch network management) and 6G use cases (such as industry 5.0). Additionally, the paper summarizes the lessons learned from recent attempts and outlines important research challenges in applying XAI for 6G use cases soon.

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“…At present, governments are opting for limited use of dashboards when it comes to smart cities and smart governance [217]. As the technology develops further, XAI will enable governments to make themselves more accountable before the public, where the public can demand transparency into decision-making and verify claimed performance, such as during election campaigns [218], movements of public concerns and social cause [219] 3 .…”

Section: • Virtual Assistants For Dynamic Customer Experiencesmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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Topy: Real-Time Story Tracking via Social Tags

Cited by 7 publications

References 4 publications

Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

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