A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Wei, Zhepei; Su, Jianlin; Tian, Yuan; Chang, Yi

doi:10.48550/arxiv.1909.03227

Cited by 23 publications

(43 citation statements)

References 25 publications

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“…In order to solve the problem of overlapping relations, Li et al (2019) proposed to treat the task of joint entity-relationship extraction as a multi-round question-and-answer type problem, but the model is more suitable for machine reading comprehension. Wei et al (2020) proposes to model relationships as functions that map head entities to tail entities, rather than treating them as labels on pairs of entities.…”

Section: Joint Methodsmentioning

confidence: 99%

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Zhang

Gao

Zhou

et al. 2022

International Journal on Semantic Web and Information Systems

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Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 ≥ 0.95) and their relations (F1 ≥ 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.

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Section: Joint Methodsmentioning

confidence: 99%

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Zhang

Gao

Zhou

et al. 2022

International Journal on Semantic Web and Information Systems

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“…With the proposed BERT model, which has achieved excellent performance on various NLP tasks, more and more studies have started to try to use the BERT model in search matching tasks and achieved very good results. In the latest study on pretrained models, Wei et al [36] achieved high metric scores using BERT. Although the BERT model has excellent encoding ability and can fully capture the semantic information of the context in the sentence, it still has problems such as high training cost and long prediction time.…”

Section: Attention Mechanisms For Relation Extractionmentioning

confidence: 99%

“…(9) CASREL BERT [36]. CASREL BERT presents cascade binary tagging framework (CASREL) and implements a new tagging framework that achieves some performance improvements.…”

Section: Comparison Modelsmentioning

confidence: 99%

Bi-GRU Relation Extraction Model Based on Keywords Attention

Zhang

et al. 2022

Data Intelligence

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Relational extraction plays an important role in the field of natural language processing to predict semantic relationships between entities in a sentence. Currently, most models have typically utilized the natural language processing tools to capture high-level features with an attention mechanism to mitigate the adverse effects of noise in sentences for the prediction results. However, in the task of relational classification, these attention mechanisms do not take full advantage of the semantic information of some keywords which have information of relational expressions in the sentences. Therefore, we propose a novel relation extraction model based on the attention mechanism with keywords, named REKA (Relation Extraction Based on Keywords Attention) in short, which incorporates an attention mechanism based on the keywords-identifiable of relation based on CRF. In particular, the proposed model makes use of bi-directional GRU (Bi-GRU) to reduce computation, obtaining the representation of sentence and extracts prior knowledge of entity pair without any NLP tools. Besides the calculation of the entity-pair similarity, Keywords attention in the REKA model also utilizes a linear-chain conditional random field (CRF) combining entity-pair features, similarity features between entity-pair features, and its hidden vectors, in order to obtain the attention weigh resulting from the marginal distribution of each word. Experiments demonstrate that the proposed approach can utilize keywords incorporating relational expression semantics in sentences without assistance of any high-level features and achieve better performance than traditional methods.

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“…Another line of work is by performing RE after NER. In the work by Miwa and Bansal (2016), the authors used BiLSTM (Graves et al, 2013) for NER and conse-quently a Tree-LSTM (Tai et al, 2015) Zeng et al, 2018Wei et al, 2019;Zhang et al, 2019) have been proposed to generate triples (i.e., node-edge-node), our model is fundamentally different from them in that: (1) it is generating a BFS/DFS traversal of the target graph, which captures dependencies between nodes and edges and has a shorter target sequence, (2) we model the nodes as the spans in the text, which is independent of the vocabulary, so even if the tokens of the nodes are rare or unseen words, we can still generate spans on them based on the context information.…”

Section: Related Workmentioning

confidence: 99%

HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction

Ren¹,

Sun²,

Ji³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets with longer input texts because of their secondorder space/time complexities with respect to the input length. In this work, we propose a Hybrid SPan GenerAtor (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. Extensive experiments on the ACE05 dataset show that our approach also significantly outperforms state-ofthe-art on the joint entity and relation extraction task. 1

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A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Cited by 23 publications

References 25 publications

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Bi-GRU Relation Extraction Model Based on Keywords Attention

HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction

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