4,711 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
Named Entity Recognition in Twitter using Images and Text
Named Entity Recognition (NER) is an important subtask of information
extraction that seeks to locate and recognise named entities. Despite recent
achievements, we still face limitations with correctly detecting and
classifying entities, prominently in short and noisy text, such as Twitter. An
important negative aspect in most of NER approaches is the high dependency on
hand-crafted features and domain-specific knowledge, necessary to achieve
state-of-the-art results. Thus, devising models to deal with such
linguistically complex contexts is still challenging. In this paper, we propose
a novel multi-level architecture that does not rely on any specific linguistic
resource or encoded rule. Unlike traditional approaches, we use features
extracted from images and text to classify named entities. Experimental tests
against state-of-the-art NER for Twitter on the Ritter dataset present
competitive results (0.59 F-measure), indicating that this approach may lead
towards better NER models.Comment: The 3rd International Workshop on Natural Language Processing for
Informal Text (NLPIT 2017), 8 page
Safeguarding Privacy Through Deep Learning Techniques
Over the last few years, there has been a growing need to meet minimum security and privacy requirements. Both public and private companies have had to comply with increasingly stringent standards, such as the ISO 27000 family of standards, or the various laws governing the management of personal data. The huge amount of data to be managed has required a huge effort from the employees who, in the absence of automatic techniques, have had to work tirelessly to achieve the certification objectives. Unfortunately, due to the delicate information contained in the documentation relating to these problems, it is difficult if not impossible to obtain material for research and study purposes on which to experiment new ideas and techniques aimed at automating processes, perhaps exploiting what is in ferment in the scientific community and linked to the fields of ontologies and artificial intelligence for data management. In order to bypass this problem, it was decided to examine data related to the medical world, which, especially for important reasons related to the health of individuals, have gradually become more and more freely accessible over time, without affecting the generality of the proposed methods, which can be reapplied to the most diverse fields in which there is a need to manage privacy-sensitive information
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