95 research outputs found
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
Entities, as the essential elements in relation extraction tasks, exhibit
certain structure. In this work, we formulate such structure as distinctive
dependencies between mention pairs. We then propose SSAN, which incorporates
these structural dependencies within the standard self-attention mechanism and
throughout the overall encoding stage. Specifically, we design two alternative
transformation modules inside each self-attention building block to produce
attentive biases so as to adaptively regularize its attention flow. Our
experiments demonstrate the usefulness of the proposed entity structure and the
effectiveness of SSAN. It significantly outperforms competitive baselines,
achieving new state-of-the-art results on three popular document-level relation
extraction datasets. We further provide ablation and visualization to show how
the entity structure guides the model for better relation extraction. Our code
is publicly available.Comment: Accepted to AAAI 202
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the
instantiation parameters of a specific type of entity. In this paper, we
explore the capsule networks used for relation extraction in a multi-instance
multi-label learning framework and propose a novel neural approach based on
capsule networks with attention mechanisms. We evaluate our method with
different benchmarks, and it is demonstrated that our method improves the
precision of the predicted relations. Particularly, we show that capsule
networks improve multiple entity pairs relation extraction.Comment: To be published in EMNLP 201
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
Extracting Entities of Interest from Comparative Product Reviews
This paper presents a deep learning based approach to extract product
comparison information out of user reviews on various e-commerce websites. Any
comparative product review has three major entities of information: the names
of the products being compared, the user opinion (predicate) and the feature or
aspect under comparison. All these informing entities are dependent on each
other and bound by the rules of the language, in the review. We observe that
their inter-dependencies can be captured well using LSTMs. We evaluate our
system on existing manually labeled datasets and observe out-performance over
the existing Semantic Role Labeling (SRL) framework popular for this task.Comment: Source Code:
https://github.com/jatinarora2702/Review-Information-Extractio
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
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