End-to-End Neural Relation Extraction Using Deep Biaffine Attention

Nguyen, Dat Quoc; Verspoor, Karin

doi:10.1007/978-3-030-15712-8_47

Cited by 48 publications

(24 citation statements)

References 25 publications

(43 reference statements)

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“…Other entities in the example document are omitted for clarity. at once (Bekoulis et al, 2018;Nguyen and Verspoor, 2019;. This does not only improve simplicity and efficiency, but is also commonly motivated by the fact that tasks can benefit from each other: For example, knowledge of two entities' types (such as person+organization) can boost certain relations between them (such as ceo of).…”

Section: Introductionmentioning

confidence: 99%

An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

Eberts

Ulges

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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We present a joint model for entity-level relation extraction from documents. In contrast to other approaches -which focus on local intra-sentence mention pairs and thus require annotations on mention level -our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. We achieve state-of-theart relation extraction results on the DocRED dataset and report the first entity-level end-toend relation extraction results for future reference. Finally, our experimental results suggest that a joint approach is on par with taskspecific learning, though more efficient due to shared parameters and training steps.

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Section: Introductionmentioning

confidence: 99%

An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

Eberts

Ulges

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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“…Based on the architecture of [10], Refs. [42,43] incorporated different types of attention, but still based on a sequence labeling scheme. Ref.…”

Section: Joint Entity and Relation Extraction Modelmentioning

confidence: 99%

Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction

Zhang¹,

Zhang²,

Ma³

2021

Applied Sciences

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Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken into consideration. It will lead to a problem that the prediction of a relation between two entities is related based on corresponding entity types, but in fact they are not related in the sentence. In addition, although previous works have proven that extract local context is beneficial for the task, it still lacks in-depth learning of contextual features in local context. In this paper, we propose to incorporate syntax knowledge into multi-head self-attention by employing part of heads to focus on syntactic parents of each token from pruned dependency trees, and we use it to model the global context to fuse syntactic and semantic features. In addition, in order to get richer contextual features from the local context, we apply local focus mechanism on entity pairs and corresponding context. Based on applying the two strategies, we perform joint entity and relation extraction on span-level. Experimental results show that our model achieves significant improvements on both Conll04 and SciERC dataset compared to strong competitors.

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“…Early approaches commonly used conditional random fields (CRF) on parse trees, representing the grammatical structure and dependencies in a sentence. Nguyen et al [48] combine modern neural BiLSTM architectures with CRFs for an end-to-end trained model to improve performance. Based on the assumption that if two entities are mentioned in the same text segment, Soares et al [73] use BERT [16] to learn relationship embeddings.…”

Section: Relationship Extraction (Relex)mentioning

confidence: 99%

Extraction and Representation of Financial Entities from Text

Repke

Krestel

2021

Data Science for Economics and Finance

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In our modern society, almost all events, processes, and decisions in a corporation are documented by internal written communication, legal filings, or business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corpora commonly used in research and highlights related work and state-of-the-art approaches to extract and represent financial entities and relations.The second part of this chapter considers applications based on knowledge graphs of automatically extracted facts. Traditional information retrieval systems typically require the user to have prior knowledge of the data. Suitable visualization techniques can overcome this requirement and enable users to explore large sets of documents. Furthermore, data mining techniques can be used to enrich or filter knowledge graphs. This information can augment source documents and guide exploration processes. Systems for document exploration are tailored to specific tasks, such as investigative work in audits or legal discovery, monitoring compliance, or providing information in a retrieval system to support decisions.

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End-to-End Neural Relation Extraction Using Deep Biaffine Attention

Cited by 48 publications

References 25 publications

An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning

Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction

Extraction and Representation of Financial Entities from Text

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