18,849 research outputs found
ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
Relation Extraction (RE) is the task of extracting semantic relationships
between entities in a sentence and aligning them to relations defined in a
vocabulary, which is generally in the form of a Knowledge Graph (KG) or an
ontology. Various approaches have been proposed so far to address this task.
However, applying these techniques to biomedical text often yields
unsatisfactory results because it is hard to infer relations directly from
sentences due to the nature of the biomedical relations. To address these
issues, we present a novel technique called ReOnto, that makes use of neuro
symbolic knowledge for the RE task. ReOnto employs a graph neural network to
acquire the sentence representation and leverages publicly accessible
ontologies as prior knowledge to identify the sentential relation between two
entities. The approach involves extracting the relation path between the two
entities from the ontology. We evaluate the effect of using symbolic knowledge
from ontologies with graph neural networks. Experimental results on two public
biomedical datasets, BioRel and ADE, show that our method outperforms all the
baselines (approximately by 3\%).Comment: Accepted in ECML 202
Graph Neural Networks with Generated Parameters for Relation Extraction
Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning
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
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
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