104,588 research outputs found
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.Comment: to appear in ACM Computing Survey
Что и как спрашивают в социальных вопросно-ответных сервисах по-русски?
In our study we surveyed different approaches to the study of questions in traditional linguistics, question answering (QA), and, recently, in community question answering (CQA). We adapted a functional-semantic classification scheme for CQA data and manually labeled 2,000 questions in Russian originating from [email protected] CQA service. About half of them are purely conversational and do not aim at obtaining actual information. In the subset of meaningful questions the major classes are requests for recommendations, or how-questions, and fact-seeking questions. The data demonstrate a variety of interrogative sentences as well as a host of formally non-interrogative expressions with the meaning of questions and requests. The observations can be of interest both for linguistics and for practical applications
Crowdsourced Rumour Identification During Emergencies
When a significant event occurs, many social media users leverage platforms such as Twitter to track that event. Moreover, emergency response agencies are increasingly looking to social media as a source of real-time information about such events. However, false information and rumours are often spread during such events, which can influence public opinion and limit the usefulness of social media for emergency management. In this paper, we present an initial study into rumour identification during emergencies using crowdsourcing. In particular, through an analysis of three tweet datasets relating to emergency events from 2014, we propose a taxonomy of tweets relating to rumours. We then perform a crowdsourced labeling experiment to determine whether crowd assessors can identify rumour-related tweets and where such labeling can fail. Our results show that overall, agreement over the tweet labels produced were high (0.7634 Fleiss Kappa), indicating that crowd-based rumour labeling is possible. However, not all tweets are of equal difficulty to assess. Indeed, we show that tweets containing disputed/controversial information tend to be some of the most difficult to identify
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