基于实例分布约束的事件语义自动划分

doi:10.3969/j.issn.0255-8297.2024.02.013

摘要/Abstract

摘要： 针对离散分布于新闻文本集合中的事件语义难以聚合的问题，提出了基于实例分布约束的事件语义自动划分算法。首先，利用远程监督方法，构建用于事件语义划分的训练数据集；其次，设计基于实例分布约束的事件语义分类器，用于判断新的事件触发词的加入是否影响事件语义的聚合；最后，在该分类器的基础上设计事件语义集合生成算法，在不需要预先设定事件类型的情况下，将分布离散的事件触发词自动地划分到不同的事件语义集合中。结果表明本方法可有效实现事件语义的自动划分，为事件语义的高质量聚合提供了一种新的探索。

关键词: 实例分布约束, 事件语义自动划分, 远程监督, 事件语义分类器, 集合生成算法

Abstract: This paper proposes an automatic event semantic division algorithm based on instance distribution constraints to address the difficulty in aggregating event semantics that are discretely distributed in news text collections. First, the distant supervision method is used to construct training dataset for event semantic division. Second, a semantic classifier based on instance constraints is designed to determine whether the addition of new event trigger affects the aggregation of event semantics. Finally, an event semantic set generation algorithm is designed based on the classifier, which can automatically divide the discrete event triggers into different event semantic sets without the need for pre-setting event types. Experimental results show that the proposed method can effectively classify event semantics, and offer a new approach for achieving high-quality aggregation of event semantics.

Key words: instance distribution constraint, automatic event semantic division, distant supervision, event semantic classifier, set generation algorithm

中图分类号:

TP391

高剑奇, 骆祥峰, 裴昕淼. 基于实例分布约束的事件语义自动划分[J]. 应用科学学报, 2024, 42(2): 323-333.

GAO Jianqi, LUO Xiangfeng, PEI Xinmiao. Automatic Event Semantic Division Based on Instance Distribution Constraints[J]. Journal of Applied Sciences, 2024, 42(2): 323-333.

参考文献

[1] Mao Q R, Li X, Peng H, et al. Event prediction based on evolutionary event ontology knowledge [J]. Future Generation Computer Systems, 2021, 115(2):76-89.
[2] Feng W Z, Wu Y, Wu W, et al. Beihang-MSRA at SemEval-2017 task 3:a ranking system with neural matching features for community question answering [C]//11th International Workshop on Semantic Evaluation (SemEval-2017), 2017:280-286.
[3] Ferracane E, Marshall I, Wallace B C, et al. Leveraging coreference to identify arms in medical abstracts:an experimental study [C]//Seventh International Workshop on Health Text Mining and Information Analysis, 2016:86-95.
[4] Bagga A, Baldwin B. Cross-document event coreference:annotations, experiments, and observations [C]//Workshop on Coreference and its Applications, 1999:1-8.
[5] Qu M, Ren X, Han J W. Automatic synonym discovery with knowledge bases [C]//23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017:997-1005.
[6] Wang W B, Thomas C, Sheth A, et al. Pattern-based synonym and antonym extraction [C]//Proceedings of the 48th Annual Southeast Regional Conference, 2010:64.
[7] Bejan C, Harabagiu S. Unsupervised event coreference resolution [J]. Computational Linguistics, 2014, 40(2):311-347.
[8] Liu Z Z, Araki J, Hovy E, et al. Supervised within-document event coreference using information propagation [J]. 9th International Conference on Language Resources and Evaluation, 2014:4539-4544.
[9] Miller G A, Beckwith R, Fellbaum C, et al. Introduction to WordNet:an on-line lexical database [J]. International Journal of Lexicography, 1990, 3(4):235-244.
[10] Baker C F, Fillmore C J, Lowe J B. The Berkeley framenet project [C]//36th Annual Meeting on Association for Computational Linguistics, 1998:86-90.
[11] Lu J, Ng V. Joint learning for event coreference resolution [C]//55th Annual Meeting on Association for Computational Linguistics, 2017:90-101.
[12] 赵云山, 段友祥. 基于Attention机制的卷积神经网络文本分类模型[J]. 应用科学学报, 2019, 37(4):541-550. Zhao Y S, Duan Y X. Convolutional neural networks text classification model based on attention mechanism [J]. Journal of Applied Sciences, 2019, 37(4):541-550. (in Chinese)
[13] Krause S, Xu F Y, Uszkoreit H, et al. Event linking with sentential features from convolutional neural networks [C]//Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, 2016:239-249.
[14] Fang J, Li P F, Zhou G D. Employing multiple decomposable attention networks to resolve event coreference [C]//CCF International Conference on Natural Language Processing and Chinese Computing. Cham:Springer, 2018:246-256.
[15] 方杰, 李培峰, 朱巧明. 基于多注意力机制的事件同指消解方法[J]. 计算机科学, 2019, 46(8):277-281. Fang J, Li P F, Zhu Q M. Employing multi-attention mechanism to resolve event coreference [J]. Computer Science, 2019, 46(8):277-281. (in Chinese)
[16] Lu Y J, Lin H, Tang J, et al. End-to-end neural event coreference resolution [J]. Artificial Intelligence, 2022, 303:103632.
[17] Lu J, Ng V. Constrained multi-task learning for event coreference resolution [C]//2021 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, 2021:4504-4514.
[18] Chen Z, Ji H. Graph-based event coreference resolution [C]//2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4), 2009:54-57.
[19] Liu Z, Mitamura T, Hovy E. Graph-based decoding for event sequencing and coreference resolution [C]//27th International Conference on Computational Linguistics, 2018:3645-3657.
[20] Nicolae C, Nicolae G. BestCut:a graph algorithm for coreference resolution [C]//2006 Conference on Empirical Methods in Natural Language Processing, 2006:275-283.
[21] Sangeetha S, Arock M. Event coreference resolution using mincut based graph clustering [J]. International Journal of Computing and Information Sciences, 2012(7):253-260.
[22] Shen J M, Lyu R L, Ren X, et al. Mining entity synonyms with efficient neural set generation [C]//AAAI Conference on Artificial Intelligence, 2019, 33(1):249-256.
[23] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [C]//Advances in Neural Information Processing Systems, 2017:5998-6008.
[24] 赵云山, 段友祥. 基于Attention机制的卷积神经网络文本分类模型[J]. 应用科学学报, 2019, 37(4):541-550 Zhao Y S, Duan Y X. Convolutional neural networks text classification model based on attention mechanism [J]. Journal of Applied Sciences, 2019, 37(4):541-550. (in Chinese)
[25] Zhao T T, Luo X F, Qin W, et al. Topic detection model in a single-domain corpus inspired by the human memory cognitive process [J]. Concurrency and Computation:Practice and Experience, 2018, 30(19):e4642.
[26] Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks [J]. Journal of Statistical Mechanics:Theory and Experiment, 2008, 2008(10):P10008.
[27] Wagstaff K, Cardie C, Rogers S, et al. Constrained K-means clustering with background knowledge [C]//International Conference on Machine Learning ICML, 2001:577-584.