6,571 research outputs found
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags
Twitter accounts have already been used in many scientometric studies, but
the meaningfulness of the data for societal impact measurements in research
evaluation has been questioned. Earlier research focused on social media counts
and neglected the interactive nature of the data. We explore a new network
approach based on Twitter data in which we compare author keywords to hashtags
as indicators of topics. We analyze the topics of tweeted publications and
compare them with the topics of all publications (tweeted and not tweeted). Our
exploratory study is based on a comprehensive publication set of climate change
research. We are interested in whether Twitter data are able to reveal topics
of public discussions which can be separated from research-focused topics. We
find that the most tweeted topics regarding climate change research focus on
the consequences of climate change for humans. Twitter users are interested in
climate change publications which forecast effects of a changing climate on the
environment and to adaptation, mitigation and management issues rather than in
the methodology of climate-change research and causes of climate change. Our
results indicate that publications using scientific jargon are less likely to
be tweeted than publications using more general keywords. Twitter networks seem
to be able to visualize public discussions about specific topics.Comment: 31 pages, 1 table, and 7 figure
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