2,803 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
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and their relations directly, without identifying entities and
relations separately. We conduct experiments on a public dataset produced by
distant supervision method and the experimental results show that the tagging
based methods are better than most of the existing pipelined and joint learning
methods. What's more, the end-to-end model proposed in this paper, achieves the
best results on the public dataset
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