Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities

Over the last decade we have made great progress in entity linking (EL) systems, but performance may vary depending on the context and, arguably, there are even principled limitations preventing a "perfect" EL system. This also suggests that there may be applications for which current "imperfect" EL is already very useful, and makes finding the "right" application as important as building the "right" EL system. We investigate the Digital Humanities use case, where scholars spend a considerable amount of time selecting relevant source texts. We developed WideNet; a semantically-enhanced search tool which leverages the strengths of (imperfect) EL without getting in the way of its expert users. We evaluate this tool in two historical case-studies aiming to collect a set of references to historical periods in parliamentary debates from the last two decades; the first targeted the Dutch Golden Age, and the second World War II. The case-studies conclude with a critical reflection on the utility of WideNet for this kind of research, after which we outline how such a real-world application can help to improve EL technology in general.Comment: Accepted for presentation at SEMANTiCS '1

WikiM: Metapaths based Wikification of Scientific Abstracts

In order to disseminate the exponential extent of knowledge being produced in the form of scientific publications, it would be best to design mechanisms that connect it with already existing rich repository of concepts -- the Wikipedia. Not only does it make scientific reading simple and easy (by connecting the involved concepts used in the scientific articles to their Wikipedia explanations) but also improves the overall quality of the article. In this paper, we present a novel metapath based method, WikiM, to efficiently wikify scientific abstracts -- a topic that has been rarely investigated in the literature. One of the prime motivations for this work comes from the observation that, wikified abstracts of scientific documents help a reader to decide better, in comparison to the plain abstracts, whether (s)he would be interested to read the full article. We perform mention extraction mostly through traditional tf-idf measures coupled with a set of smart filters. The entity linking heavily leverages on the rich citation and author publication networks. Our observation is that various metapaths defined over these networks can significantly enhance the overall performance of the system. For mention extraction and entity linking, we outperform most of the competing state-of-the-art techniques by a large margin arriving at precision values of 72.42% and 73.8% respectively over a dataset from the ACL Anthology Network. In order to establish the robustness of our scheme, we wikify three other datasets and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for the mention extraction and the entity linking phase

Multicast broadcast services support in OFDMA-based WiMAX systems [Advances in mobile multimedia]

Multimedia stream service provided by broadband wireless networks has emerged as an important technology and has attracted much attention. An all-IP network architecture with reliable high-throughput air interface makes orthogonal frequency division multiplexing access (OFDMA)-based mobile worldwide interoperability for microwave access (mobile WiMAX) a viable technology for wireless multimedia services, such as voice over IP (VoIP), mobile TV, and so on. One of the main features in a WiMAX MAC layer is that it can provide'differentiated services among different traffic categories with individual QoS requirements. In this article, we first give an overview of the key aspects of WiMAX and describe multimedia broadcast multicast service (MBMS) architecture of the 3GPP. Then, we propose a multicast and broadcast service (MBS) architecture for WiMAX that is based on MBMS. Moreover, we enhance the MBS architecture for mobile WiMAX to overcome the shortcoming of limited video broadcast performance over the baseline MBS model. We also give examples to demonstrate that the proposed architecture can support better mobility and offer higher power efficiency

Team Learning, Development, and Adaptation

[Excerpt] Our purpose is to explore conceptually these themes centered on team learning, development, and adaptation. We note at the onset that this chapter is not a comprehensive review of the literature. Indeed, solid conceptual and empirical work on these themes are sparse relative to the vast amount of work on team effectiveness more generally, and therefore a thematic set of topics that are ripe for conceptual development and integration. We draw on an ongoing stream of theory development and research in these areas to integrate and sculpt a distinct perspective on team learning, development, and adaptation

Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur