37,428 research outputs found
Nanoinformatics: developing new computing applications for nanomedicine
Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others
Crowdsourced real-world sensing: sentiment analysis and the real-time web
The advent of the real-time web is proving both challeng-
ing and at the same time disruptive for a number of areas of research,
notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis
and discusses the motivations and challenges behind such a direction
Detecting Large Concept Extensions for Conceptual Analysis
When performing a conceptual analysis of a concept, philosophers are
interested in all forms of expression of a concept in a text---be it direct or
indirect, explicit or implicit. In this paper, we experiment with topic-based
methods of automating the detection of concept expressions in order to
facilitate philosophical conceptual analysis. We propose six methods based on
LDA, and evaluate them on a new corpus of court decision that we had annotated
by experts and non-experts. Our results indicate that these methods can yield
important improvements over the keyword heuristic, which is often used as a
concept detection heuristic in many contexts. While more work remains to be
done, this indicates that detecting concepts through topics can serve as a
general-purpose method for at least some forms of concept expression that are
not captured using naive keyword approaches
Topically Driven Neural Language Model
Language models are typically applied at the sentence level, without access
to the broader document context. We present a neural language model that
incorporates document context in the form of a topic model-like architecture,
thus providing a succinct representation of the broader document context
outside of the current sentence. Experiments over a range of datasets
demonstrate that our model outperforms a pure sentence-based model in terms of
language model perplexity, and leads to topics that are potentially more
coherent than those produced by a standard LDA topic model. Our model also has
the ability to generate related sentences for a topic, providing another way to
interpret topics.Comment: 11 pages, Proceedings of the 55th Annual Meeting of the Association
for Computational Linguistics (ACL 2017) (to appear
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