Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

By Suncong Zheng, Feng Wang, Hongyun Bao, Peng Zhou, Bo Xu. Annual Meeting of the Association for Computational Linguistics, 2017

707 citations recorded by the source corpus.

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.

View Paper: https://www.semanticscholar.org/paper/f5d6779073901989f14de984e2236891a801c2bd

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Citations in the corpus

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (2018, methodology): “…recent neural models for these tasks focus on single tasks (Clark and Manning, 2016; Wiseman et al., 2016; Lee et al., 2017; Lam-ple et al., 2016; Peng et al., 2017) or joint entity and relation extraction (Katiyar and Cardie, 2017; Zhang et al., 2017; Adel and Sch¨utze, 2017; Zheng et al., 2017).”
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2019, methodology): “…the framework where all parameters of BERT are randomly initialized; C AS R EL LSTM is the framework instantiated on a LSTM-based structure as in (Zheng et al., 2017) with pre-trained Glove embedding (Penning-ton et al., 2014); C AS R EL is the full-ﬂedged framework using pre-trained BERT…”
Unified Structure Generation for Universal Information Extraction (2022, methodology): “IE is a long-researched area and many classical neural architectures have been proposed, such as sequence tagging (Lample et al., 2016; Zheng et al., 2017; Lin et al., 2019), span classification (Sohrab and Miwa, 2018; Lin et al.”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Some convert joint extraction into different problems such as sequence labeling via a novel tagging scheme [168] and multi-turn question answering [169].”
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (2019, methodology): “We compared GraphRel with two baselines: Nov-elTagging (Zheng et al., 2017) and MultiDe-coder (Zeng et al., 2018).”
A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “A few studies [86]–[88], [94], [130] have explored RNN to decode tags.”
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism (2018, methodology): “In this paper, we treat this dataset as supervised data as the same as Zheng et al. (2017).”
A Frustratingly Easy Approach for Entity and Relation Extraction (2021, methodology): “…entities, Zhang et al. (2017a); Wang and Lu (2020) adopt a table-ﬁlling approach proposed in (Miwa and Sasaki, 2014); Katiyar and Cardie (2017) and Zheng et al. (2017) employ sequence tagging-based approaches; Sun et al. (2019) and Fu et al. (2019) propose graph-based approaches to jointly…”
A Joint Neural Model for Information Extraction with Global Features (2020, methodology): “Some recent efforts develop joint neural models to perform extraction of two IE subtasks, such as entity and relation extraction (Zheng et al., 2017; Katiyar and Cardie, 2017; Bekoulis et al., 2018; Fu et al., 2019; Luan et al., 2019; Sun et al., 2019) and event and temporal relation extraction…”
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2020, background): “As a result, the predicted tokens at training and inference are drawn from different distributions, namely, from the data distribution as opposed to the model distribution(Zhang et al., 2019).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “[221] extend BIES labels with the relationship types and roles of a word (e.”