[1] MINTZ M, BILLS S, SNOW R, et al.Distant supervision for relation extraction without labeled data[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP.New York, USA:ACM Press, 2009:1003-1011. [2] ZENG D J, LIU K, CHEN Y B, et al.Distant supervision for relation extraction via piecewise convolutional neural networks[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Stroudsburg, USA:Association for Computational Linguistics, 2015:1-10. [3] LIN Y K, SHEN S Q, LIU Z Y, et al.Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Stroudsburg, USA:Association for Computational Linguistics, 2016:1-10. [4] HOFFMANN R, ZHANG C L, LING X, et al.Knowledge-based weak supervision for information extraction of overlapping relations[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies.New York, USA:ACM Press, 2011:541-550. [5] SURDEANU M, TIBSHIRANI J, NALLAPATI R, et al.Multi-instance multi-label learning for relation extraction[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.New York, USA:ACM Press, 2012:455-465. [6] JAT S, KHANDELWAL S, TALUKDAR P.Improving distantly supervised relation extraction using word and entity based attention[EB/OL].[2022-01-02].https://arxiv.org/abs/1804.06987. [7] YANG Z Y, WANG L, MA B, et al.RTJTN:relational triplet joint tagging network for joint entity and relation extraction[J].Computational Intelligence and Neuroscience, 2021, 2021:3447473. [8] YE Z X, LING Z H.Distant supervision relation extraction with intra-bag and inter-bag attentions[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg, USA:Association for Computational Linguistics, 2019:1-10. [9] WU S C, FAN K, ZHANG Q.Improving distantly supervised relation extraction with neural noise converter and conditional optimal selector[J].Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):7273-7280. [10] LI P S, ZHANG X S, JIA W J, et al.GAN driven semi-distant supervision for relation extraction[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg, USA:Association for Computational Linguistics, 2019:3026-3035. [11] HAN X, LIU Z Y, SUN M S.Denoising distant supervision for relation extraction via instance-level adversarial training[EB/OL].[2022-01-02].https://arxiv.org/abs/1805.10959. [12] HUANG P S, HE X D, GAO J F, et al.Learning deep structured semantic models for Web search using clickthrough data[C]//Proceedings of the 22nd ACM International Conference on Information & Knowledge Management.New York, USA:ACM Press, 2013:2333-2338. [13] SEVERYN A, MOSCHITTI A.Learning to rank short text pairs with convolutional deep neural networks[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York, USA:ACM Press, 2015:373-382. [14] YIN W P, SCHÜTZE H, XIANG B, et al.ABCNN:attention-based convolutional neural network for modeling sentence pairs[J].Transactions of the Association for Computational Linguistics, 2016, 4:259-272. [15] WANG Z G, HAMZA W, FLORIAN R.Bilateral multi-perspective matching for natural language sentences[EB/OL].[2022-01-02].https://arxiv.org/abs/1702.03814. [16] CHEN Q, ZHU X D, LING Z H, et al.Enhanced LSTM for natural language inference[EB/OL].[2022-01-02].https://arxiv.org/abs/1609.06038. [17] GONG Y C, LUO H, ZHANG J.Natural language inference over interaction space[EB/OL].[2022-01-02].https://arxiv.org/abs/1709.04348. [18] KIM S, KANG I, KWAK N.Semantic sentence matching with densely-connected recurrent and co-attentive information[J].Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):6586-6593. [19] HUANG G, LIU Z, VAN DER MAATEN L, et al.Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:2261-2269. [20] ANWAR A, LI X, YANG Y T, et al.Constructing uyghur named entity recognition system using neural machine translation tag projection[C]//Proceedings of China National Conference on Chinese Computational Linguistics.Berlin, Germany:Springer, 2020:247-260. [21] SHAW P, USZKOREIT J, VASWANI A.Self-attention with relative position representations[EB/OL].[2022-01-02].https://arxiv.org/abs/1803.02155. [22] LU J S, YANG J W, BATRA D, et al.Hierarchical question-image co-attention for visual question answering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.New York, USA:ACM Press, 2016:289-297. [23] ARTETXE M, SCHWENK H.Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond[J].Transactions of the Association for Computational Linguistics, 2019, 7:597-610. [24] GRAVES A, SCHMIDHUBER J.Framewise phoneme classification with bidirectional LSTM networks[C]//Proceedings of 2005 IEEE International Joint Conference on Neural Networks.Washington D.C., USA:IEEE Press, 2005:2047-2052. [25] LAMPLE G, CONNEAU A.Cross-lingual language model pretraining[EB/OL].[2022-01-02].https://arxiv.org/abs/1901.07291. [26] DEVLIN J, CHANG M W, LEE K, et al.BERT:pre-training of deep bidirectional Transformers for language understanding[EB/OL].[2022-01-02].https://arxiv.org/pdf/1810.04805.pdf. [27] CONNEAU A, KHANDELWAL K, GOYAL N, et al.Unsupervised cross-lingual representation learning at scale[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg, USA:Association for Computational Linguistics, 2020:1-10. [28] LIU Y H, OTT M, GOYAL N, et al.RoBERTa:a robustly optimized BERT pretraining approach[EB/OL].[2022-01-02].https://arxiv.org/abs/1907.11692. [29] CASACUBERTA F, VIDAL E.GIZA++:training of statistical translation models[C]//Proceedings of Workshop on Multi-Lingual Speech Communication.Kyoto, Japan:[s.n.], 2000:69-74. |