491 research outputs found

    Extracting semantic entities and events from sports tweets

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    Large volumes of user-generated content on practically every major issue and event are being created on the microblogging site Twitter. This content can be combined and processed to detect events, entities and popular moods to feed various knowledge-intensive practical applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. In this paper, we exploit various approaches to detect the named entities and significant micro-events from users’ tweets during a live sports event. Here we describe how combining linguistic features with background knowledge and the use of Twitter-specific features can achieve high, precise detection results (f-measure = 87%) in different datasets. A study was conducted on tweets from cricket matches in the ICC World Cup in order to augment the event-related non-textual media with collective intelligence

    Towards a continuous modeling of natural language domains

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    Humans continuously adapt their style and language to a variety of domains. However, a reliable definition of `domain' has eluded researchers thus far. Additionally, the notion of discrete domains stands in contrast to the multiplicity of heterogeneous domains that humans navigate, many of which overlap. In order to better understand the change and variation of human language, we draw on research in domain adaptation and extend the notion of discrete domains to the continuous spectrum. We propose representation learning-based models that can adapt to continuous domains and detail how these can be used to investigate variation in language. To this end, we propose to use dialogue modeling as a test bed due to its proximity to language modeling and its social component.Comment: 5 pages, 3 figures, published in Uphill Battles in Language Processing workshop, EMNLP 201

    A lightweight web video model with content and context descriptions for integration with linked data

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    The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

    Privacy-the Civil Liberties Issue

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    Exploratory Study of Ventilated Flows About Yawed Surface-Piercing Struts

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    Reported herein are the results of observations and measurements made in connection with a study of the phenomenon of the development of atmosphere-connected cavities about surface-piercing struts. Conditions for the existence of such ventilated flows which have been derived from the experimental data are presented. In addition, certain broad conclusions pertinent to model testing and full-scale design are reached. Further experimentation to define the inception of ventilation as a function of boundary-layer state or Reynolds number is required
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