Citation: | WANG Jianing, HE Yi, ZHU Renyu, LIU Tingting, GAO Ming. Relation extraction via distant supervision technology[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 113-130. doi: 10.3969/j.issn.1000-5641.202091006 |
[1] |
刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述 [J]. 计算机研究与发展, 2016, 53(3): 582-600.
|
[2] |
KEJRIWAL M, SEQUEDA J, LOPEZ V, et al. Knowledge graphs: Construction, management and querying: Editorial [J]. Social Work, 2019, 10(6): 961-962.
|
[3] |
YU M, YIN W, HASAN K S, et al. Improved neural relation detection for knowledge base question answering [C]// Meeting of the Association for Computational Linguistics. 2017: 571-581.
|
[4] |
ALLAHYARI M, POURIYEH S, ASSEFI M, et al. Text summarization techniques: A brief survey [J]. International Journal of Advanced Computer Science and Applications, 2017, 8(10): 397-405.
|
[5] |
HASEGAWA T, SEKINE S, GRISHMAN R, et al. Discovering relations among named entities from large corpora [C]// Meeting of the Association for Computational Linguistics. 2004: 415-422.
|
[6] |
ETZIONI O, BANKO M, SODERLAND S, et al. Open information extraction from the web [J]. Communications of the ACM, 2008, 51(12): 68-74.
|
[7] |
LI F, ZHANG M, FU G, et al. A Bi-LSTM-RNN model for relation classification using low-cost sequence features[J]. ArXiv: Computation and Language, 2016.
|
[8] |
姚春华, 刘潇, 高弘毅, 等. 基于句法语义特征的实体关系抽取技术 [J]. 通信技术, 2018, 51(8): 1828-1835.
|
[9] |
KUMLIEN M C J. Constructing biological knowledge bases by extraction information from text sources [C]// Proc Int Conf Intell Syst Mol Biol. 1999: 77-86.
|
[10] |
MINTZ M, BILLS S, SNOW R, et al. Distant supervision for relation extraction without labeled data [C]// International Joint Conference on Natural Language Processing. 2009: 1003-1011.
|
[11] |
ZENG X, HE S, LIU K, et al. Large scaled relation extraction with reinforcement learning [C]// National Conference on Artificial Intelligence. 2018: 5658-5665.
|
[12] |
杨东明, 杨大为, 顾航, 等. 面向初等数学的知识点关系提取研究 [J]. 华东师范大学学报(自然科学版), 2019(5): 53-65.
|
[13] |
RIEDEL S, YAO L, MCCALLUM A, et al. Modeling relations and their mentions without labeled text [C]// European Conference on Machine Learning. 2010: 148-163.
|
[14] |
BOLLACKER K, EVANS C, PARITOSH P, et al. Freebase: A collaboratively created graph database for structuring human knowledge [C]// International Conference on Management of Data. 2008: 1247-1250.
|
[15] |
JAT S, KHANDELWAL S, TALUKDAR P P, et al. Improving distantly supervised relation extraction using word and entity based attention [J]. ArXiv: Computation and Language, 2018.
|
[16] |
HAN X, ZHU H, YU P, et al. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation [C]// Empirical Methods in Natural Language Processing. 2018: 4803-4809.
|
[17] |
ZENG D, LIU K, CHEN Y, et al. Distant supervision for relation extraction via piecewise convolutional neural networks [C]// Empirical Methods in Natural Language Processing. 2015: 1753-1762.
|
[18] |
ZELENKO D, AONE C, RICHARDELLA A, et al. Kernel methods for relation extraction [J]. Journal of Machine Learning Research, 2003, 3(6): 1083-1106.
|
[19] |
SHI G, FENG C, HUANG L, et al. Genre separation network with adversarial training for cross-genre relation extraction [C]// Empirical Methods in Natural Language Processing. 2018: 1018-1023.
|
[20] |
VASHISHTH S, JOSHI R, PRAYAGA S S, et al. RESIDE: Improving distantly-supervised neural relation extraction using side information [C]// Empirical Methods in Natural Language Processing. 2018: 1257-1266.
|
[21] |
LI Y, LONG G, SHEN T, et al. Self-attention enhanced selective gate with entity-aware embedding for distantly supervised relation extraction [C]// National Conference on Artificial Intelligence. 2020.
|
[22] |
KUANG J, CAO Y, ZHENG J, et al. Improving neural relation extraction with implicit mutual relations [C]// International Conference on Data Engineering. 2020.
|
[23] |
KRAUSE S, LI H, USZKOREIT H, et al. Large-scale learning of relation-extraction rules with distant supervision from the web [C]// International Semantic Web Conference. 2012: 263-278.
|
[24] |
白龙, 靳小龙, 席鹏弼, 等. 基于远程监督的关系抽取研究综述 [J]. 中文信息学报, 2019, 33(10): 10-17.
|
[25] |
鄂海红, 张文静, 肖思琪, 等. 深度学习实体关系抽取研究综述 [J]. 软件学报, 2019, 30(6): 1793-1818.
|
[26] |
SUCHANEK F M, KASNECI G, WEIKUM G, et al. Yago: A core of semantic knowledge [C]// The Web Conference. 2007: 697-706.
|
[27] |
ZHOU P, SHI W, TIAN J, et al. Attention-based bidirectional long short-term memory networks for relation classification [C]// Meeting of the Association for Computational Linguistics. 2016: 207-212.
|
[28] |
HOFFMANN R, ZHANG C, LING X, et al. Knowledge-based weak supervision for information extraction of overlapping relations [C]// Meeting of the Association for Computational Linguistics. 2011: 541-550.
|
[29] |
SURDEANU M, TIBSHIRANI J, NALLAPATI R, et al. Multi-instance multi-label learning for relation extraction [C]// Empirical Methods in Natural Language Processing. 2012: 455-465.
|
[30] |
TAKAMATSU S, SATO I, NAKAGAWA H, et al. Reducing wrong labels in distant supervision for relation extraction [C]// Meeting of the Association for Computational Linguistics. 2012: 721-729.
|
[31] |
FAN M, ZHAO D, ZHOU Q, et al. Distant supervision for relation extraction with matrix completion [C]// Meeting of the Association for Computational Linguistics. 2014: 839-849.
|
[32] |
ZHANG Q, WANG H. Noise-clustered distant supervision for relation extraction: A nonparametric bayesian perspective [C]// Empirical Methods in Natural Language Processing. 2017: 1808-1813.
|
[33] |
MIN B, GRISHMAN R, WAN L, et al. Distant supervision for relation extraction with an incomplete knowledge base [C]// North American Chapter of the Association for Computational Linguistics. 2013: 777-782.
|
[34] |
XU W, HOFFMANN R, ZHAO L, et al. Filling knowledge base gaps for distant supervision of relation extraction [C]// Meeting of the Association for Computational Linguistics. 2013: 665-670.
|
[35] |
RITTER A, ZETTLEMOYER L, ETZIONI O, et al. Modeling missing data in distant supervision for information extraction [C]// Transactions of the Association for Computational Linguistics. 2013: 367-378.
|
[36] |
LIN Y, SHEN S, LIU Z, et al. Neural relation extraction with selective attention over instances [C]// Meeting of the Association for Computational Linguistics. 2016: 2124-2133.
|
[37] |
JI G, LIU K, HE S, et al. Distant supervision for relation extraction with sentence-level attention and entity descriptions [C]// National Conference on Artificial Intelligence. 2017: 3060-3066.
|
[38] |
JAT S, KHANDELWAL S, TALUKDAR P P, et al. Improving distantly supervised relation extraction using word and entity based attention [J]. ArXiv: Computation and Language, 2018.
|
[39] |
WU S, FAN K, ZHANG Q, et al. Improving distantly supervised relation extraction with neural noise converter and conditional optimal selector [J]. National Conference on Artificial Intelligence, 2019, 33(1): 7273-7280.
|
[40] |
YE Z, LING Z. Distant supervision relation extraction with intra-bag and inter-bag attentions [C]// North American Chapter of the Association for Computational Linguistics. 2019: 2810-2819.
|
[41] |
YUAN Y, LIU L, TANG S, et al. Cross-relation cross-bag attention for distantly-supervised relation extraction [J]. National Conference on Artificial Intelligence, 2019, 33(1): 419-426.
|
[42] |
JIA W, DAI D, XIAO X, et al. ARNOR: Attention regularization based noise reduction for distant supervision relation classification [C]// Meeting of the Association for Computational Linguistics. 2019: 1399-1408.
|
[43] |
ALT C, HUBNER M, HENNIG L, et al. Fine-tuning pre-trained transformer language models to distantly supervised relation extraction [C]// Meeting of the Association for Computational Linguistics. 2019: 1388-1398.
|
[44] |
WU Y, BAMMAN D, RUSSELL S, et al. Adversarial training for relation extraction [C]// Empirical Methods in Natural Language Processing. 2017: 1778-1783.
|
[45] |
QIN P, WEIRAN X U, WANG W Y, et al. DSGAN: Generative adversarial training for robust distant supervision relation extraction [C]// Meeting of the Association for Computational Linguistics. 2018: 496-505.
|
[46] |
LI P, ZHANG X, JIA W, et al. GAN driven semi-distant supervision for relation extraction [C]// North American Chapter of the Association for Computational Linguistics. 2019: 3026-3035.
|
[47] |
HAN X, LIU Z, SUN M, et al. Denoising distant supervision for relation extraction via instance-level adversarial training [J]. ArXiv: Computation and Language, 2018.
|
[48] |
FENG J, HUANG M, ZHAO L, et al. Reinforcement learning for relation classification from noisy data [C]// National Conference on Artificial Intelligence. 2018: 5779-5786.
|
[49] |
HE Z, CHEN W, WANG Y, et al. Improving neural relation extraction with positive and unlabeled learning [C]// National Conference on Artificial Intelligence. 2020.
|
[50] |
QIN P, XU W, WANG W Y, et al. Robust distant supervision relation extraction via deep reinforcement learning [C]// Meeting of the Association for Computational Linguistics. 2018: 2137-2147.
|
[51] |
SU Y, LIU H, YAVUZ S, et al. Global relation embedding for relation extraction [C]// North American Chapter of the Association for Computational Linguistics. 2018: 820-830.
|
[52] |
XU P, BARBOSA D. Investigations on knowledge base embedding for relation prediction and extraction [J]. ArXiv: Computation and Language, 2018.
|
[53] |
XU P, BARBOSA D. Connecting language and knowledge with heterogeneous representations for neural relation extraction [C]// North American Chapter of the Association for Computational Linguistics. 2019: 3201-3206.
|
[54] |
LIU Y, LIU K, XU L, et al. Exploring fine-grained entity type constraints for distantly supervised relation extraction [C]// International Conference on Computational Linguistics. 2014: 2107-2116.
|
[55] |
YE Y, FENG Y, LUO B, et al. Integrating relation constraints with neural relation extractors [C]// National Conference on Artificial Intelligence. 2020.
|
[56] |
BELTAGY I, LO K, AMMAR W, et al. Combining distant and direct supervision for neural relation extraction [C]// North American Chapter of the Association for Computational Linguistics. 2019: 1858-1867.
|
[57] |
WEI Z, SU J, WANG Y, et al. A novel hierarchical binary tagging framework for joint extraction of entities and relations [J]. ArXiv: Computation and Language, 2019.
|
[58] |
REN X, WU Z, HE W, et al. CoType: Joint extraction of typed entities and relations with knowledge bases [C]// The Web Conference. 2017: 1015-1024.
|
[59] |
TAKANOBU R, ZHANG T, LIU J, et al. A hierarchical framework for relation extraction with reinforcement learning [J]. National Conference on Artificial Intelligence, 2019, 33(1): 7072-7079.
|
[60] |
YE W, LI B, XIE R, et al. Exploiting entity BIO tag embeddings and multi-task learning for relation extraction with imbalanced data [C]// Meeting of the Association for Computational Linguistics. 2019: 1351-1360.
|
[61] |
GUI Y, LIU Q, ZHU M, et al. Exploring long tail data in distantly supervised relation extraction [C]// LIN C Y, XUE N, ZHAO D, et al. Natural Language Understanding and Intelligent Applications. ICCPOL 2016, NLPCC 2016. Lecture Notes in Computer Science, 2016.
|
[62] |
ZHANG N, DENG S, SUN Z, et al. Long-tail relation extraction via knowledge graph embeddings and graph convolution networks [C]// North American Chapter of the Association for Computational Linguistics. 2019: 3016-3025.
|
[63] |
HAN X, YU P, LIU Z, et al. Hierarchical relation extraction with coarse-to-fine grained attention [C]// Empirical Methods in Natural Language Processing. 2018: 2236-2245.
|
[64] |
MIKOLOV T, CHEN K, CORRADO G S, et al. Efficient estimation of word representations in vector space [C]// International Conference on Learning Representations. 2013.
|
[65] |
PENNINGTON J, SOCHER R, MANNING C D, et al. Glove: Global vectors for word representation [C]// Empirical Methods in Natural Language Processing. 2014: 1532-1543.
|
[66] |
DEVLIN J, CHANG M, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding [C]// North American Chapter of the Association for Computational Linguistics. 2019: 4171-4186.
|
[67] |
GOODFELLOW I, POUGETABADIE J, MIRZA M, et al. Generative adversarial nets [C]// Neural Information Processing Systems. 2014: 2672-2680.
|
[68] |
SALVARIS M, DEAN D, TOK W H, et al. Generative adversarial networks [J]. ArXiv: Machine Learning, 2018: 187-208.
|
[69] |
ANDREW A M. Reinforcement learning: An introduction [J]. Kybernetes, 1998, 27(9): 1093-1096.
|
[70] |
SUN T, ZHANG C, JI Y, et al. Reinforcement learning for distantly supervised relation extraction [J]. IEEE Access, 2019(7): 98023-98033.
|
[71] |
TANG J, QU M, WANG M, et al. LINE: Large-scale information network embedding [C]// The Web Conference. 2015: 1067-1077.
|
[72] |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.
|
[73] |
BORDES A, USUNIER N, GARCIADURAN A, et al. Translating embeddings for modeling multi-relational data [C]// Neural Information Processing Systems. 2013: 2787-2795.
|
[74] |
KIPF T, WELLING M. Semi-supervised classification with graph convolutional networks [C]// International Conference on Learning Representations. 2017.
|
[75] |
HENDRICKX I, KIM S N, KOZAREVA Z, et al. SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals [C]// North American Chapter of the Association for Computational Linguistics. 2009: 94-99.
|
[76] |
SURDEANU M, GUPTA S, BAUER J, et al. Stanford's distantly-supervised slot-filling system [R]. Stanford, CA: Stanford University, 2011.
|
[77] |
JI, HENG, GRISHMAN, RALPH, et al. Overview of the TAC 2010 knowledge base population track [C]// Text Analysis Conference. 2009.
|
[78] |
JI H, GRISHMAN R, DANG H. Overview of the TAC2011 knowledge base population track [C]// Text Analysis Conference. 2011.
|
[79] |
GAO T, HAN X, ZHU H, et al. FewRel 2.0: Towards more challenging few-shot relation classification [C]// International Joint Conference on Natural Language Processing. 2019: 6249-6254.
|
[80] |
XU J, WEN J, SUN X, et al. A discourse-level named entity recognition and relation extraction dataset for Chinese literature text [J]. ArXiv: Computation and Language, 2017.
|
[81] |
HAN X, GAO T, YAO Y, et al. OpenNRE: An open and extensible toolkit for neural relation extraction [C]// International Joint Conference on Natural Language Processing. 2019: 169-174.
|
[82] |
LIU T, ZHANG X, ZHOU W, et al. Neural relation extraction via inner-sentence noise reduction and transfer learning [C]// Empirical Methods in Natural Language Processing. 2018: 2195-2204.
|
[83] |
REN Z, WANG X, ZHANG N, et al. Deep reinforcement learning-based image captioning with embedding reward [C]// Computer Vision and Pattern Recognition. 2017: 1151-1159.
|
[84] |
SHANG Y M, HUANG H, MAO X, et al. Are noisy sentences useless for distant supervised relation extraction [C]// National Conference on Artificial Intelligence. 2020.
|
[85] |
CAO Z, HIDALGO G, SIMON T, et al. OpenPose: Realtime multi-person 2D pose estimation using part affinity fields [J]. ArXiv: Computer Vision and Pattern Recognition, 2018.
|