2,305 research outputs found
People on Drugs: Credibility of User Statements in Health Communities
Online health communities are a valuable source of information for patients
and physicians. However, such user-generated resources are often plagued by
inaccuracies and misinformation. In this work we propose a method for
automatically establishing the credibility of user-generated medical statements
and the trustworthiness of their authors by exploiting linguistic cues and
distant supervision from expert sources. To this end we introduce a
probabilistic graphical model that jointly learns user trustworthiness,
statement credibility, and language objectivity. We apply this methodology to
the task of extracting rare or unknown side-effects of medical drugs --- this
being one of the problems where large scale non-expert data has the potential
to complement expert medical knowledge. We show that our method can reliably
extract side-effects and filter out false statements, while identifying
trustworthy users that are likely to contribute valuable medical information
A cross-benchmark comparison of 87 learning to rank methods
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered by the absence of a standard set of evaluation benchmark collections. In this paper we propose a way to compare learning to rank methods based on a sparse set of evaluation results on a set of benchmark datasets. Our comparison methodology consists of two components: (1) Normalized Winning Number, which gives insight in the ranking accuracy of the learning to rank method, and (2) Ideal Winning Number, which gives insight in the degree of certainty concerning its ranking accuracy. Evaluation results of 87 learning to rank methods on 20 well-known benchmark datasets are collected through a structured literature search. ListNet, SmoothRank, FenchelRank, FSMRank, LRUF and LARF are Pareto optimal learning to rank methods in the Normalized Winning Number and Ideal Winning Number dimensions, listed in increasing order of Normalized Winning Number and decreasing order of Ideal Winning Number
Leveraging Self-Supervised Training for Unintentional Action Recognition
Unintentional actions are rare occurrences that are difficult to defineprecisely and that are highly dependent on the temporal context of the action.In this work, we explore such actions and seek to identify the points in videoswhere the actions transition from intentional to unintentional. We propose amulti-stage framework that exploits inherent biases such as motion speed,motion direction, and order to recognize unintentional actions. To enhancerepresentations via self-supervised training for the task of unintentionalaction recognition we propose temporal transformations, called TemporalTransformations of Inherent Biases of Unintentional Actions (T2IBUA). Themulti-stage approach models the temporal information on both the level ofindividual frames and full clips. These enhanced representations show strongperformance for unintentional action recognition tasks. We provide an extensiveablation study of our framework and report results that significantly improveover the state-of-the-art.<br
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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
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