计算机科学 ›› 2023, Vol. 50 ›› Issue (9): 295-302.doi: 10.11896/jsjkx.220700041
衡红军, 苗菁
HENG Hongjun, MIAO Jing
摘要： 实体关系抽取任务是信息抽取的核心任务,它对于有效地从爆炸性增长的数据中提取出关键性的信息有着不可替代的作用,也是构建大规模知识图谱的基础任务,因此研究实体关系抽取对各种自然语言处理任务具有重要意义。尽管现有的基于深度学习方法的实体关系抽取已经有了很成熟的理论和较好的性能,但依然还存在着误差累积、实体冗余、交互缺失、三元组重叠等问题。语义信息和句法信息对自然语言处理任务都具有重要作用,为了充分利用这些信息以解决上述提到的问题,提出了一种融合语义和句法图神经网络的二元标记实体关系联合抽取模型FSSRel(Fusion of Semantic and Syntactic Graph Convolutional Networks Binary Tagging Framework for Relation triple extraction)。该模型分为三个阶段进行:第一阶段,对三元组主体的开始结束位置进行预测标记;第二阶段,分别通过语义图神经网络和句法图神经网络提取语义特征和句法特征,并将其融合进编码向量;第三阶段,对语句的每种关系的客体位置进行预测标记,完成最终三元组的提取。实验结果表明,在NYT数据集和WebNLG数据集上,该模型的F1值较基线模型分别提升了2.5%和1.6%,并且在拥有重叠三元组和多三元组等问题的复杂数据上也有良好的表现。
中图分类号:
| [1]LI D M,ZHANG Y,LI D Y,et al.Review of entity relation extraction methods [J].Journal of Computer Research and Deve-lopment,2020,57(7):1424-1448. [2]SHEN Y,MA X,TANG Y,et al.A Trigger-Sense MemoryFlow Framework for Joint Entity and Relation Extraction[C]//Proceedings of the Web Conference 2021.2021:1704-1715. [3]WU L,CHEN Y,SHEN K,et al.Graph neural networks fornatural language processing:A survey[J].arXiv:2106.06090,2021. [4]WEI Z,SU J,WANG Y,et al.A novel cascade binary tagging framework for relational triple extraction[J].arXiv:1909.03227,2019. [5]TIAN Y,CHEN G,SONG Y,et al.Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2021:4458-4471. [6]MIWA M,BANSAL M.End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2016. [7]EBERTS M,ULGES A.Span-based joint entity and relationextraction with transformer pre-training[J].arXiv:1909.07755,2019. [8]ZENG X,ZENG D,HE S,et al.Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2018. [9]BEKOULIS G,DELEU J,DEMEESTER T,et al.Joint entity recognition and relation extraction as a multi-head selection problem[J].Expert Systems with Applications,2018,114:34-45. [10]YU B,ZHANG Z,SHU X,et al.Joint extraction of entities and relations based on a novel decomposition strategy[J].arXiv:1909.04273,2019. [11]NIU W,CHEN Q,ZHANG W,et al.GCN2-NAA:Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction[C]//ICMLC 2021:2021 13th International Conference on Machine Learning and Computing.2021. [12]YAN Z,ZHANG C,FU J,et al.A Partition Filter Network for Joint Entity and Relation Extraction[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.2021:185-197. [13]ZHENG S,F WANG,BAO H,et al.Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme[C]//Procee-dings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2017. [14]WANG J,LU W.Two are better than one:Joint entity and relation extraction with table-sequence encoders[J].arXiv:2010.03851,2020. [15]SUI D,CHEN Y,LIU K,et al.Joint Entity and Relation Extraction with Set Prediction Networks[J].arXiv:2011.01675,2020. [16]VASWANI A,SHAZEER N,PARMAR N,et al.Attention Is All You Need[J].arXiv:1706.03762,2017. [17]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018. [18]MANNING C,SURDEANU M,BAUER J,et al.The stanford core NLP natural language processing toolkit[C]//Proceedings of the 52th Annual Meeting of Association for Computational Linguistics:System Demonstrations.Stroudsburg:ACL Press,2014:55-60. [19]RIEDEL S,YAO L,MCCALLUM A.Modeling relations andtheir mentions without labeled text[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Berlin:Springer,2010:148-163. [20]GARDENT C,SHIMORINA A,NARAYAN S,et al.Creating training corpora for nlg micro-planning[C]//55th Annual Mee-ting of the Association for Computational Linguistics(ACL).2017. [21]FU T J,LI P H,MA W Y.Graphrel:Modeling text as relational graphs for joint entity and relation extraction[C]//Proceedings of the 57th Annual Meeting of the Association for Computa-tional Linguistics.2019:1409-1418. [22]BAI C,PAN L,LUO S,et al.Joint extraction of entities and relations by a novel end-to-end model with a double-pointer mo-dule[J].Neurocomputing,2020,377:325-333. [23]ZENG X,HE S,ZENG D,et al.Learning the extraction order of multiple relational facts in a sentence with reinforcement lear-ning[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:367-377. [24]HONG Y,LIU Y,YANG S,et al.Improving graph convolu-tional networks based on relation-aware attention for end-to-end relation extraction[J].IEEE Access,2020,8:51315-51323. |
|
||
首页