DPMN：面向重叠关系抽取问题的多任务学习网络

doi:10.3778/j.issn.1002-8331.2307-0047

摘要/Abstract

摘要： 作为自然语言处理的基本组件之一，关系抽取旨在从给定的非结构化文本中抽取关系事实。针对实际应用中句子级的实体信息缺失，往往会出现单个句子包含多个重叠关系三元组的场景，关系三元组会产生多种交叉重叠，使得关系抽取任务具有较大的挑战性。早期的研究采用流水线方法来处理，不仅忽略实体识别和关系预测的关联性，而且容易受到误差传播问题的影响。提出了一种基于依赖解析的多任务学习网络（DPMN），以依赖解析的方式更精确地识别实体跨度、丰富关系语义，具有多任务学习策略，以增强各个子任务之间的交互。与基线模型相比，DPMN具有更好的关系三元组抽取性能，一定程度上缓解了关系重叠问题。

关键词: 关系抽取, 多任务学习, 依赖解析, 重叠关系

Abstract: As one of the basic components of natural language processing, relation extraction aims to extract relation facts from a given unstructured text. In practical applications, there is a lack of entity information at the sentence level, and there are often scenarios where a single sentence contains multiple overlapping relation triplets. Relation triplets can generate multiple cross overlaps, making relation extraction tasks more challenging. Early research uses pipeline method to process, which not only ignores the relevance of entity recognition and relationship prediction, but also is vulnerable to propagation of uncertainty. This paper proposes a multi-task learning network (DPMN) based on dependency parsing, which can identify entity span more accurately by dependency parsing, enrich relation semantics, and have multi-task learning strategies to enhance the interaction between various subtasks. Compared with the baseline model, DPMN has better performance in relation triplet extraction, which alleviates the problem of overlapping relations to some extent.

Key words: relation extraction, multi-task learning, dependency parsing, overlapping relation

李雅杰, 唐国根, 李平. DPMN：面向重叠关系抽取问题的多任务学习网络[J]. 计算机工程与应用, 2024, 60(20): 160-167.

LI Yajie, TANG Guogen, LI Ping. DPMN：Multi-Task Learning Network for Problem of Overlapping Relation Extraction[J]. Computer Engineering and Applications, 2024, 60(20): 160-167.

参考文献

[1] ZHOU G D, SU J, ZHANG J, et al. Exploring various knowledge in relation extraction[C]//Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, 2005: 427-434.
[2] YEE S C, DAN R. Exploiting syntactico-semantic structures for relation extraction[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011: 551-560.
[3] DMITRY Z, CHINATSU A, ANTHONY R. Kernel methods for relation extraction[J]. Journal of Machine Learning Research, 2003, 3: 1083-1106.
[4] BOWMAN S R, POTTS C, MANNING C D. Recursive neural networks for learning logical semantics[J]. arXiv:1406.1827, 2014.
[5] KATIYAR A, CARDIE C. Going out on a limb: joint extraction of entity mentions and relations without dependency trees[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 917-928.
[6] HONG Y, LIU Y X, YANG S Z, et al. Improving graph convolutional networks based on relation-aware attention for end-to-end relation extraction[J]. IEEE Access, 2020, 8: 51315-51323.
[7] YU B W, ZAHNG Z Y, SHU X B, et al. Joint extraction of entities and relations based on a novel decomposition strategy[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 1572-1582.
[8] ZHENG H Y, RUI W, CHEN X, et al. PRGC: potential relation and global correspondence based joint relational triple extraction[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 6225-6235.
[9] ZENG X R, ZENG D J, HE S Z, 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, 2018: 506-514.
[10] 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 Computational Linguistics, 2019: 1409-1418.
[11] WEI Z P, SU J L, WANG Y, et al. A novel cascade binary tagging framework for relational triple extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 1476-1488.
[12] SHEN Y L, MA X Y, TANG Y C, et al. A trigger-sense memory flow framework for joint entity and relation extraction[C]//Proceedings of the Web Conference, 2021: 1704-1715.
[13] WANG L Y, XIONG C, DENG N. A research on overlapping relationship extraction based on multi-objective dependency[C]//Proceedings of the 15th International Conference on Computer Science & Education, 2020: 618-622.
[14] WANG Y C, YU B W, ZHANG Y Y, et al. TPLinker: single-stage joint extraction of entities and relations through token pair linking[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 1572-1582.
[15] ZHENG S C, WANG F, BAO H Y, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1227-1236.
[16] MIWA M, BANSAL M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2016: 1105-1116.
[17] ZHAO K, XU H, CHENG Y, et al. Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction[J]. Knowledge Based Systems, 2021, 219: 106888.
[18] ZHAO F B, JIANG Z R, KANG Y Y, et al. Adjacency list oriented relational fact extraction via adaptive multi-task learning[J]. arXiv:2106.01559, 2021.
[19] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[20] ZHENG H L, FU J L, ZHA Z J, et al. Learning deep bilinear transformation for fine-grained image representation[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems, 2019: 4277-4286.
[21] VINYALS O, FORTUNATO M, JAITLY N. Pointer networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 2692-2700.
[22] RIEDEL S, YAO L, MCCALLUM A. Modeling relations and their mentions without labeled text[C]//Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases, 2010: 148-163.
[23] GARDENT C, SHIMORINA A, NARAYAN S, et al. Creating training corpora for NLG micro-planning[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017.
[24] ZENG X R, HE S Z, ZENG D J, et al. Learning the extraction order of multiple relational facts in a sentence with reinforcement learning[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 367-377.
[25] 郝小芳, 张超群, 李晓翔, 等. 融合交互注意力网络的实体和关系联合抽取模型[J]. 计算机工程与应用, 2024, 60(8): 156-164.
HAO X F, ZHANG C Q, LI X X, et al. Joint entity relation extraction model based on interactive attention[J]. Computer Engineering and Applications, 2024, 60(8): 156-164.
[26] XU B F, WANG Q, LYU Y J, et al. EmRel: joint representation of entities and embedded relations for multi-triple extraction[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022: 659-665.
[27] WELLING M, KIPF T N. Semi-supervised classification with graph convolutional networks[C]//Proceedings of the International Conference on Learning Representations, 2017.
[28] SHANG Y M, HUANG H Y, MAO X L. OneRel: joint entity and relation extraction with one module in one step[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022: 11285-11293.
[29] HSU I H, HUANG K H, BOSCHEE E, et al. Degree: a data-efficient generation-based event extraction model[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022: 1890-1908.