连动句是具备连动结构的句子,是汉语中一种特殊的句法结构,在现代汉语中十分常见且使用频繁。连动句语法结构和语义关系都很复杂,在识别中存在许多问题,对此该文针对连动句的识别问题进行了研究,提出了一种基于神经网络的连动句识别方法。该方法分两步: 第一步,运用简单的规则对语料进行预处理;第二步,利用文本分类的思想,使用BERT编码,利用多层CNN与BiLSTM模型联合提取特征进行分类,进而完成连动句识别任务。在人工标注的语料上进行实验,实验结果达到92.71%的准确率,F1值为87.41%。
Abstract
Serial-verb sentence is a sentence with several coordinated verbs. The grammatical structure and semantic relationship of serial-verb sentences are very complicated, which brings obstacles in its automatic recognition. This paper proposes a recognition model based on neural networks for the recognition of serial-verb sentence. This method uses rules to preprocess the corpus and then applies BERT, the multi-layer CNN and the BiLSTM model to jointly extract features for classification, and then complete the sentence recognition task. Experimental results show that our model achieves an accuracy of 92.71% and F1-value of 87.41%.
关键词
连动句 /
文本分类 /
神经网络 /
抽象语义表示
{{custom_keyword}} /
Key words
serial-verb sentence /
text classification /
neural network /
abstract meaning representation
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 刘雯旻.基于汉语连动句的常识获取方法研究[D].镇江:江苏科技大学硕士学位论文,2017.
[2] 洪淼.现代汉语连动结构研究[D].南京:南京师范大学博士学位论文,2004.
[3] 韩志玲,倪蓉.原型理论启发下的现代汉语连动句类型研究[J].上海理工大学学报(社会科学版),2012,34(01): 41-45.
[4] 彭国珍,杨晓东,赵逸亚.国内外连动结构研究综述[J].当代语言学,2013,15(03): 324-335.
[5] 洪淼.现代汉语连动句式的语义结构研究[J].西南民族大学学报(人文社科版),2004(07): 423-426.
[6] 许有胜.连动结构研究综述[J].兰州学刊,2007(09): 137-142.
[7] 陈波,姬东鸿,吕晨.基于特征结构的汉语连动句语义标注研究[J].中文信息学报,2013,27(05): 60-66.
[8] 曲维光,周俊生,吴晓东,等.自然语言句子抽象语义表示AMR研究综述[J].数据采集与处理,2017,32(1): 26-36.
[9] 李斌,闻媛,宋丽,等.融合概念对齐信息的中文AMR语料库的构建[J].中文信息学报,2017,31(06): 93-102.
[10] 许有胜.连动结构的自动识别和分析[J].巢湖学院学报,2013,15(04): 108-115.
[11] 刘雯旻, 张晓如. 一种基于规则和统计的连动句识别
方法[J].电子设计工程,2017,25(22): 18-22.
[12] Kim Y. Convolutional neural networks for sentence classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2014: 1746-1751.
[13] Lai S, Xu L, Liu K, et al. Recurrent convolutional neural networks for text classification[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence,2015: 2267-2273.
[14] Conneau A, Schwenk H, Barrault L,et al. Very deep convolutional networks for text classification[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics,2017:1107-1116.
[15] Liu P,Qiu X, Huang X. Recurrent neural network for text classification with multi-task learning[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence,2016: 2873-2879.
[16] Wang B. Disconnected recurrent neural networks for text categorization[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, 2018: 2311-2320.
[17] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2019: 4171-4186.
[18] Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J/OL]. arXiv preprint arXiv:1412.3555,2014.
[19] Schuster M,Paliwal K K. Bidirectional recurrent neural network[J].IEEE Transactions on Signal Processing,1997,45(11):2673-2681.
[20] Wang J, Wang Z, Zhang D, et al. Combining knowledge with deep convolutional neural networks for short text classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence,2017: 2915-2921.
[21] Wang S, Huang M, Deng Z. Densely connected cnn with multi-scale feature attention for text classification[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence,2018:4468-4474.
[22] 范晓.汉语的句子类型[M].太原: 书海出版社,1980,23-78.
[23] Joulin A, Grave E, Bojanowski P, et al. Bag of tricks for efficient text classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics,2016:427-431.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金(61772278);国家社会科学基金(21&ZD288,18BYY127);教育部人文社会科学研究青年基金(17YJC740084)
{{custom_fund}}
PDF(1915 KB)
