Recently, a new field of computer science was derived, including
methods and techniques of problem solving that cannot be easily
described by traditional algorithms. This field, called ""cognitive
computing"" or ""real-world computing"", has a varied set of
working methodologies, such as: fuzzy logic, approximate reasoning,
genetic algorithms, chaos theory, and the Artificial Neural
Networks (ANN). The objective of the present work is to introduce
the problematic of the latter: definitions, principles and
typology, as well as concrete applications in the field of
information retrieval. During the past decade in the field of
information retrieval has been experimented with artificial
Intelligence (AI) techniques based on rules and knowledge. These
techniques seem to have many limitations and difficulties of
application, so that already in the present decade work has begun
with the more recent. AI techniques, based on inductive learning:
symbolic learning, genetic algorithms and neural networks (Chen,
1995). The earliest work in neural computing dates back to the
early 1940s, which neuro-physicist Warren McCulloch and
mathematician Walter Pitts proposed, based on their system studies.
A formal neuron model implemented by electrical circuits
(McMulloch, 1943), whose enthusiasm aroused the neuronal model
drove research in this line during the 1950s and 1960s. In 1957
Frank Rosenblatt developed the Perceptron, a network model that
possesses the generalization capability, so it has been used to
this day in various applications, generally in recognition of
patterns. In 1959 Bernard Widrow and Marcial Hoff of Stanford
University developed the model ADALINE (ADAptative LINear
Elements), first ANN applied to a real problem (noise filters in
lines phone calls). In 1969 Marvin Minsky and Seymour Papert, of
MIT, published a work in which they attack the neural model and
consider that any research along these lines was sterile (Minsky,
1969). Due to this criticism the works on ANN stop to a new impetus
during the 80's. Despite this pause, several researchers continued
to work in that direction during the 1970s. Such is the case of the
American James Anderson which develops the BSB
(Brain-State-in-a-Box) model, or Finnish Teuvo Kohonen who does the
same with one based on self-organizing maps. As of 1982 the
interest for the neuronal computation began to take force again.
The progress made in hardware and software, methodological advances
around learning algorithms for ANN, and the new techniques of
artificial intelligence, favored this rebirth. The same year, the
first conference between neuronal computing researchers from the US
and Japan. In 1985 the American Institute of physics establishes
annual meeting Neural Networks for Computing. In 1987 the IEEE held
the first conference on ANN. That same year the International
Society of Neural Networks was created (INNS). An automatic
learning system that identifies the expressions of denial and
speculation in biomedical texts is presented, specifically in the
collection of BioScope documents. The objective of the work is to
compare the efficiency of this approach centered in automatic
learning with which it is based on regular expressions. Between the
systems that follow this latter approach, we have used NegEx
because of its availability and popularity. The evaluation has been
carried out on the three subcollections that form BioScope:
clinical documents, scientific articles and abstracts of scientific
articles. The results show the superiority of the approach based on
automatic learning regarding the use of regular expressions. In the
identification of negation expressions, the system improves the F1
measure of NegEx between 20 and 30%, depending on the collection of
documents. In the identification of speculation, the proposed
system exceeds the measure F1 of the best baseline algorithm
between 10 and 20%.
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