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              融合實(shí)體和上下文信息的篇章關(guān)系抽取研究

              黃河燕 袁長(cháng)森 馮沖

              黃河燕, 袁長(cháng)森, 馮沖. 融合實(shí)體和上下文信息的篇章關(guān)系抽取研究. 自動(dòng)化學(xué)報, 2024, 50(10): 1953?1962 doi: 10.16383/j.aas.c220966
              引用本文: 黃河燕, 袁長(cháng)森, 馮沖. 融合實(shí)體和上下文信息的篇章關(guān)系抽取研究. 自動(dòng)化學(xué)報, 2024, 50(10): 1953?1962 doi: 10.16383/j.aas.c220966
              Huang He-Yan, Yuan Chang-Sen, Feng Chong. Document-level relation extraction with entity and context information. Acta Automatica Sinica, 2024, 50(10): 1953?1962 doi: 10.16383/j.aas.c220966
              Citation: Huang He-Yan, Yuan Chang-Sen, Feng Chong. Document-level relation extraction with entity and context information. Acta Automatica Sinica, 2024, 50(10): 1953?1962 doi: 10.16383/j.aas.c220966

              融合實(shí)體和上下文信息的篇章關(guān)系抽取研究

              doi: 10.16383/j.aas.c220966
              詳細信息
                作者簡(jiǎn)介:

                黃河燕:北京理工大學(xué)計算機學(xué)院教授. 主要研究方向為語(yǔ)言信息智能化處理, 社交網(wǎng)絡(luò ), 數據分析和云計算. E-mail: hhy63@bit.edu.cn

                袁長(cháng)森:北京理工大學(xué)計算機學(xué)院博士后. 主要研究方向為知識圖譜, 信息抽取. 本文通信作者. E-mail: yuanchangsen@bit.edu.cn

                馮沖:北京理工大學(xué)計算機學(xué)院教授. 主要研究方向為機器翻譯, 信息抽取和信息檢索. E-mail: fengchong@bit.edu.cn

              Document-level Relation Extraction With Entity and Context Information

              More Information
                Author Bio:

                HUANG He-Yan Professor at the School of Computer Science and Technology, Beijing Institute of Technology. Her research interest covers intelligent processing of language information, social network, data analysis, and cloud computing

                YUAN Chang-Sen Postdoctor at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers knowledge graph and information extraction. Corresponding author of this paper

                FENG Chong Professor at the School of Computer Science and Technology, Beijing Institute of Technology. His research interest covers machine translation, information extraction, and information retrieval

              • 摘要: 篇章關(guān)系抽取旨在識別篇章中實(shí)體對之間的關(guān)系. 相較于傳統的句子級別關(guān)系抽取, 篇章級別關(guān)系抽取任務(wù)更加貼近實(shí)際應用, 但是它對實(shí)體對的跨句子推理和上下文信息感知等問(wèn)題提出了新的挑戰. 本文提出融合實(shí)體和上下文信息(Fuse entity and context information, FECI)的篇章關(guān)系抽取方法, 它包含兩個(gè)模塊, 分別是實(shí)體信息抽取模塊和上下文信息抽取模塊. 實(shí)體信息抽取模塊從兩個(gè)實(shí)體中自動(dòng)地抽取出能夠表示實(shí)體對關(guān)系的特征. 上下文信息抽取模塊根據實(shí)體對的提及位置信息, 從篇章中抽取不同的上下文關(guān)系特征. 本文在三個(gè)篇章級別的關(guān)系抽取數據集上進(jìn)行實(shí)驗, 效果得到顯著(zhù)提升.
              • 圖  1  篇章級別關(guān)系抽取數據集DocRED中的一個(gè)實(shí)例

                Fig.  1  An example of document-level relation extraction dataset DocRED

                圖  2  模型框架圖主要有兩個(gè)部分, 分別是實(shí)體信息抽取模塊和上下文信息抽取模塊

                Fig.  2  Architecture of the proposed model, which contains two parts: Entity information extraction module and context information extraction module

                圖  3  篇章級別關(guān)系抽取開(kāi)發(fā)集中的一個(gè)實(shí)例分析

                Fig.  3  An example analysis on the document-level relation extraction development set

                表  1  數據集的統計

                Table  1  Statistics of the datasets

                統計DocREDCDRGDA
                訓練集305350023353
                開(kāi)發(fā)集10005005839
                測試集10005001000
                關(guān)系種類(lèi)9722
                每篇的關(guān)系數量19.57.65.4
                下載: 導出CSV

                表  2  模型的超參數

                Table  2  Hyper-parameters of model

                參數名稱(chēng)DocREDCDRGDA
                批次大小444
                迭代次數303010
                學(xué)習率 (編碼)$5\times 10^{-5}$$5\times 10^{-5}$$5\times 10^{-5}$
                學(xué)習率 (分類(lèi))$1\times 10^{-4}$$1\times 10^{-4}$$1\times 10^{-4}$
                分組大小646464
                Dropout0.10.10.1
                梯度裁剪1.01.01.0
                下載: 導出CSV

                表  3  在DocRED開(kāi)發(fā)集和測試集上的實(shí)驗結果(%)

                Table  3  Experiment results on the development and test sets of DocRED (%)

                模型開(kāi)發(fā)集測試集
                Ign F1F1 Ign F1F1
                CNN41.5843.4540.3342.26
                LSTM48.4450.6847.7150.07
                Bi-LSTM48.8750.9448.7851.06
                Context-Aware48.9451.0948.4050.70
                HIN-GloVe51.0652.9551.1553.30
                GAT-GloVe45.1751.4447.3649.51
                GCNN-GloVe46.2251.5249.5951.62
                EoG-GloVe45.9452.1549.4851.82
                AGGCN-GloVe46.2952.4748.8951.45
                LSR-GloVe48.8255.1752.1554.18
                BERT-REBASE54.1653.20
                RoBERTaBASE53.8556.0553.5255.77
                BERT-Two-StepBASE54.4253.92
                HIN-BERTBASE54.2956.3153.7055.60
                CorefBERTBASE55.3257.5154.5456.96
                LSR-BERTBASE52.4359.0056.9759.05
                BERT-EBASE56.5158.52
                GAINBASE59.1461.2259.0061.24
                FECIBASE59.7461.3859.8161.22
                下載: 導出CSV

                表  4  在CDR和GDA數據集上測試集F1值(%)

                Table  4  F1 values of test set on CDR and GDA datasets (%)

                模型CDRGDA
                BRAN62.1
                CNN62.3
                EoG63.681.5
                LSR-BERT64.882.2
                SciBERTBASE65.182.5
                SciBERT-EBASE65.983.3
                FECIBASE 69.283.7
                下載: 導出CSV

                表  5  FECIBASE在開(kāi)發(fā)集上的消融研究結果

                Table  5  Ablation study results of FECIBASE on the development set

                模型開(kāi)發(fā)集
                Ign F1 (%)F1 (%)P (M)T (s)
                FECIBASE59.7461.38133.42962.4
                w/o Entity58.1660.07132.22831.7
                w/o Context58.6760.89130.5482.3
                下載: 導出CSV

                表  6  FECIBASE在開(kāi)發(fā)集上噪聲實(shí)體和噪聲上下文的實(shí)驗結果(%)

                Table  6  The experiment results of noisy entity and noisy context of FECIBASE on the development set (%)

                模型開(kāi)發(fā)集
                Ign F1F1
                FECIBASE59.7461.38
                Head entity58.4260.14
                Tail entity57.9760.08
                Entity pair58.9160.85
                Tradition57.4259.72
                Co-occurrence58.2761.01
                Non co-occurrence56.7258.86
                下載: 導出CSV

                表  7  FECIBASE在開(kāi)發(fā)集上不同上下文信息的實(shí)驗結果(%)

                Table  7  The experiment results of different context information of FECIBASE on the development set (%)

                模型開(kāi)發(fā)集
                Ign F1F1
                FECIBASE59.7461.38
                Random58.4760.61
                Mean59.5660.94
                Tradition58.1960.06
                下載: 導出CSV

                表  8  不同方法在開(kāi)發(fā)集上的效率

                Table  8  Efficiency of different methods on the development set

                模型開(kāi)發(fā)集
                P (M)Train T (s)Decoder T (s)
                LSR-BERTBASE112.1282.938.8
                GAINBASE217.02271.6817.2
                FECIBASE133.42962.4829.0
                下載: 導出CSV
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                        • 收稿日期:  2022-12-12
                        • 錄用日期:  2023-03-29
                        • 網(wǎng)絡(luò )出版日期:  2023-08-28
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