1,032 research outputs found
N-ary Relation Extraction using Graph State LSTM
Cross-sentence -ary relation extraction detects relations among
entities across multiple sentences. Typical methods formulate an input as a
\textit{document graph}, integrating various intra-sentential and
inter-sentential dependencies. The current state-of-the-art method splits the
input graph into two DAGs, adopting a DAG-structured LSTM for each. Though
being able to model rich linguistic knowledge by leveraging graph edges,
important information can be lost in the splitting procedure. We propose a
graph-state LSTM model, which uses a parallel state to model each word,
recurrently enriching state values via message passing. Compared with DAG
LSTMs, our graph LSTM keeps the original graph structure, and speeds up
computation by allowing more parallelization. On a standard benchmark, our
model shows the best result in the literature.Comment: EMNLP 18 camera read
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
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
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