6,187 research outputs found
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
A text-mining system for extracting metabolic reactions from full-text articles
Background: Increasingly biological text mining research is focusing on the extraction of complex relationships
relevant to the construction and curation of biological networks and pathways. However, one important category of
pathway—metabolic pathways—has been largely neglected.
Here we present a relatively simple method for extracting metabolic reaction information from free text that scores
different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence
and location of stemmed keywords. This method extends an approach that has proved effective in the context of the
extraction of protein–protein interactions.
Results: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our
method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the
well-known protein-protein interaction extraction task.
Conclusions: We conclude that automated metabolic pathway construction is more tractable than has often been
assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed
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
Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
We participated in three of the protein-protein interaction subtasks of the
Second BioCreative Challenge: classification of abstracts relevant for
protein-protein interaction (IAS), discovery of protein pairs (IPS) and text
passages characterizing protein interaction (ISS) in full text documents. We
approached the abstract classification task with a novel, lightweight linear
model inspired by spam-detection techniques, as well as an uncertainty-based
integration scheme. We also used a Support Vector Machine and the Singular
Value Decomposition on the same features for comparison purposes. Our approach
to the full text subtasks (protein pair and passage identification) includes a
feature expansion method based on word-proximity networks. Our approach to the
abstract classification task (IAS) was among the top submissions for this task
in terms of the measures of performance used in the challenge evaluation
(accuracy, F-score and AUC). We also report on a web-tool we produced using our
approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our
approach to the full text tasks resulted in one of the highest recall rates as
well as mean reciprocal rank of correct passages. Our approach to abstract
classification shows that a simple linear model, using relatively few features,
is capable of generalizing and uncovering the conceptual nature of
protein-protein interaction from the bibliome. Since the novel approach is
based on a very lightweight linear model, it can be easily ported and applied
to similar problems. In full text problems, the expansion of word features with
word-proximity networks is shown to be useful, though the need for some
improvements is discussed
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