6,129 research outputs found
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
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
On the Use of Parsing for Named Entity Recognition
[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/01This work has been funded by MINECO, AEI and FEDER of UE through the ANSWER-ASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the ConsellerÃa de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the SecretarÃa Xeral de Universidades (Ref. ED431G 2019/01). Carlos Gómez-RodrÃguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, Grant No. 714150)
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