融入中文语义信息及越南语句法特征的越南语事件检测方法

PDF(2762 KB)

中文信息学报 ›› 2022, Vol. 36 ›› Issue (8) : 62-72.

民族、跨境及周边语言信息处理

融入中文语义信息及越南语句法特征的越南语事件检测方法

陈龙^1,2,郭军军^1,2,张亚飞^1,2,高盛祥^1,2,余正涛^1,2

作者信息 +

Vietnamese Event Detection Method Incroporating Chinese Semantic Information and Vietnamese Syntactic Featues

CHEN Long^1,2, GUO Junjun^1,2, ZHANG Yafei^1,2, GAO Shengxiang^1,2, YU Zhengtao^1,2

Author information +

History +

摘要

当前基于深度学习的事件检测模型都依赖足够数量的标注数据,而标注数据的稀缺及事件类型歧义为越南语事件检测带来了极大的挑战。根据“表达相同观点但语言不同的句子通常有相同或相似的语义成分”这一多语言一致性特征,该文提出了一种融入中文语义信息及越南语句法特征的越南语事件检测框架。首先通过共享编码器策略和交叉注意力网络将中文信息融入越南语中,然后使用图卷积网络融入越南语依存句法信息,最后在中文事件类型指导下实现越南语事件检测。实验结果表明,在中文语义信息和越南语句法特征的指导下越南语事件检测取得了较好的效果。

Abstract

Current event detection models based on deep learning rely on labeled data. However, the scarcity of annotation data for Vietnamese events and the ambiguity of event types have brought challenges to Vietnamese event detection.Taking advantage of the fact that sentences expressing the same viewpoint in different languages usually have the same or similar semantic components, this paper proposes a Vietnamese event detection framework that combines Chinese information and Vietnamese syntax. First, the shared encoder strategy and cross-attention network are applied to integrate Chinese semantic information into Vietnamese. Then the graph convolutional network is used to obtain Vietnamese representation based on Vietnamese dependency syntactic information. Finally, the Vietnamese semantic representation based on Chinese event type information is extracted through the event type perception network to realize Vietnamese news event detection. Experimental results show that the proposed method has achieved good results.

导出引用

陈龙,郭军军,张亚飞,高盛祥,余正涛. 融入中文语义信息及越南语句法特征的越南语事件检测方法. 中文信息学报. 2022, 36(8): 62-72

CHEN Long, GUO Junjun, ZHANG Yafei, GAO Shengxiang, YU Zhengtao. Vietnamese Event Detection Method Incroporating Chinese Semantic Information and Vietnamese Syntactic Featues. Journal of Chinese Information Processing. 2022, 36(8): 62-72

参考文献

[1] Li Q, Ji H, Huang L. Joint event extraction via structured prediction with global features[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013: 73-82.
[2] Liu S L, Li Y, Zhang F, et al. Event detection without triggers[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguitics: Human Language Technologies, 2019: 735-744.
[3] Chen Y M, Chen T F, Ebner S, et al. Reading the manual: Event extraction as definition comprehension[C]//Proceedings of the 4th Workshop on Structured Prediction for NLP, 2020: 74-83.
[4] Chen Z, Ji H. Can one language bootstrap the other: a case study on event extraction[C]//Proceedings of the NAACL HLT Workshop on Semi-Supervised Learning for Natural Language Processing, 2009: 66-74.
[5] Zhu Z, Li S S, Zhou G D, et al. Bilingual event extraction: A case study on trigger type determination[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014: 842-847.
[6] Faruqui M, Kumar S. Multilingual open relation extraction using cross-lingual projection[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2015: 1351-1356.
[7] Zou B W, Xu Z Z, Hong Y, et al. Adversarial feature adaptation for cross-lingual relation classification[C]//Proceedings of the 27th International Conference on Computational Linguistics,2018: 437-448.
[8] McDonald R, Nivre J, Quirmbach-Brundage Y, et al. Universal dependency annotation for multilingual parsing[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013: 92-97.
[9] Ji H, Grishman R. Refining event extraction through cross-document inference[C]//Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2008: 254-262.
[10] Liao S S, Grishman R. Using document level cross-event inference to improve event extraction[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010: 789-797.
[11] Chen Y B, Xu L H, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks [C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 167-176.
[12] Yang B S, Mitchell T M. Joint extraction of events and entities within a document context[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2016: 289-299.
[13] Keith K, Handler A, Pinkham M, et al. Identifying civilians killed by police with distantly supervised entity-event extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2017:1547-1557.
[14] Liu S L, Chen Y B, He S Z, et al. Leveraging framenet to improve automatic event detection[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 2134-2143.
[15] Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2016: 300-309.
[16] Nguyen T H, Ralph Grishman. Event detection and domain adaptation with convolutional neural networks [C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 365-371.
[17] Liu S L, Liu K, He S Z, et al. A probabilistic soft logic based approach to exploiting latent and global information in event classification[C]//Proceedings of the 13th AAAI Conference on Artificial Intelligence, 2016:2993-2999.
[18] Liu S L, Chen Y B, Liu K, et al. Exploiting argument information to improve event detection via supervised attention mechanisms[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017:1789-1798.
[19] Nguyen T H, Grishman R. Graph convolutional networks with argument-aware pooling for event detection[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 13th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence, 2018: 5900-5907.
[20] Liu X, Luo Z C, Huang H Y. Jointly multiple events extraction via attention-based graph information aggregation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018: 1247-1256.
[21] Du X Y, Cardie C. Event extraction by answering (almost) natural questions[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020: 671-683.
[22] Hsi A, Yang Y M, Carbonell J, et al. Leveraging multilingual training for limited resource event extraction[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, 2016: 1201-1210.
[23] Guo J, Che W X, Yarowsky D, et al. Cross-lingual dependency parsing based on distributed representations[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 1234-1244.
[24] Mayhew S, Tsai C T, Roth D. Cheap translation for cross-lingual named entity recognition[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2017: 2536-2545.
[25] Ni J, Dinu G, Florian R. Weakly supervised cross-lingual named entity recognition via effective annotation and representation projection[C]//Proceed-ings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1470-1480.
[26] Subburathinam A, Lu D, Ji H, et al. Cross-lingual structure transfer for relation and event extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 313-325.
[27] Liu J, Chen Y B, Liu K, et al. Event detection via gated multilingual attention mechanism[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 13th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence, 2018: 4865-4872.
[28] Liu J, Chen Y B, Liu K, et al. Neural cross-lingual event detection with minimal parallel resources[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 738-748.
[29] Vu T, Nguyen D Q, Nguyen D D, et al. VnCoreNLP: A vietnamese natural language processing toolkit[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, 2018: 56-60.

基金

国家自然科学基金(61762056, 61972186, 61732005, 61761026);国家重点研发计划(2018YFC0830105, 2018YFC0830101, 2018YFC0830100);云南高科技人才项目(201606);云南省重大科技专项计划(202002AD080001-5);云南省基础研究计划(202001AS070014,2018FB104)

PDF(2762 KB)

1195

Accesses

Citation

Detail

段落导航

摘要
Abstract
关键词
Key words
引用本文
参考文献
基金

Received	Published
2022-02-21	2022-09-26
Issue Date
2022-09-26

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Key words

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

{{custom_fnGroup.title_cn}}

脚注

基金