|Table of Contents|

Quantity Noun Phrase Structure Recognition Based on Combined Deep Learning Model(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年01期
Page:
127-135
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Quantity Noun Phrase Structure Recognition Based on Combined Deep Learning Model
Author(s):
Shi Hanyu1; Qu Weiguang1; 2; Wei Tingxin2; 3; Zhou Junsheng1; Gu Yanhui1
(1.School of Computer and Electronic Information/School of Artificial Intelligence,Nanjing Normal University,Nanjing 210023,China)(2.School of Chinese Language and Literature,Nanjing Normal University,Nanjing 210097,China)(3.International College for Chinese Studies,Nanjing Normal University,Nanjing 210097,China)
Keywords:
the recognition of quantity noun phrases; BERT; lattice LSTM; CRF
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2022.01.018
Abstract:
The research on recognition of quantity noun phrases is the identity of the left and right boundaries of quantity noun phrases. In previous studies,this task focuses on the recognition of quantity phrase and relies on artifical features which are constructed by experts based on statistical learning models. In this paper,we aim at the recognition of quantity noun phrases which have 8 subtypes and propose a neural network model to address the issue. Firstly,BERT is used to represent the contextual features of the original text. Then,the standard word segmentation is incorporated into the feature representation of the text character level as a soft feature by using the idea of Lattice LSTM model. Finally,the left and right boundaries of the“quantity noun phrase”are identified by the CRF global constraint. The experimental results show that this method achieves the better results and the precision,recall and F1 value reaches 80.83%,89.78%,85.07% respectively in the corpus of CAMR.

References:

[1] 黎锦熙. 论现代汉语中的量词[M]. 北京:商务印书馆,1978.
[2]白晓革,李义杰. 数量短语的构成模式及其识别[C]//第三届HNC与语言学研究学术研讨会论文集,北京,2005:171-178.
[3]张玲,熊文,李义杰,等. 基于知识库的现代汉语数量短语的识别[C]//第七届中文信息处理国际会议论文集,武汉,2007:295-299.
[4]熊文,张玲. 一种基于规则不依赖于分词的中文数量短语的识别[C]//第七届中文信息处理国际会议论文集,武汉,2007:36-40.
[5]方芳,李斌. 基于语料库的数量名短语识别[C]//第三届学生计算语言学研讨会论文集,沈阳,2006:331-337.
[6]PINHEIRO P H O,COLLOBERT R. Recurrent convolutional neural networks for scene parsing[EB/OL].(2013-06-12)
[2019-11-4]. //https://arxiv.org/abs/1306.2795.
[7]HUANG Z H,XU W,YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL].(2015-08-09)
[2019-11-4]. https://arxiv.org/abs/1508.01991.
[8]CHIU J P C,NICHOLS E. Named entity recognition with bidirectional LSTM-CNNs[J]. Transaction of the association of computational linguistics,2016(4):357-370.
[9]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.
[10]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(6):93-102.
[11]PETERS M E,NEUMANN M,LYYER M,et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,New Orleans,Louisiana,United States of America. 2018:2227-2237.
[12]DEVLIN J,CHANG M W,LEE K,et al. BERT:Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,2019(1):4171-4186.
[13]VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30:Annual Conference on Neural Information Processing Systems,2017:5998-6008.
[14]HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural computation,1997,9(8):1735-1780.
[15]ZHANG Y,YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics,2018:1554-1564.
[16]LAFFERTY J D,MCCALLUM A,PEREIRA F C N. Conditional random fields:probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning,2001:282-289.
[17]RATINOV L,ROTH D. Design challenges and misconceptions in named entity recognition[C]//Proceedings of the Thirteenth Conference on Computational Natural Language Learning,2009:147-155.
[18]SRIVASTAVA N,HINTON G E,KRIZHEVSKY A,et al. Dropout:a simple way to prevent neural networks from overfitting[J]. Journay machine learning research,2014,15(1):1929-1958.
[19]ZEILER M D. ADADELTA:an adaptive learning rate method[EB/OL].(2012-12-22)
[2019-11-4]. //https://arxiv.org/abs/1212.5701.
[20]KINGMA D P,BA J. Adam:a method for stochastic optimization[C]//3rd International Conference on Learning Representations,2015:arXiv:1412.6980.
[21]DAUPHIN Y N,VRIES H D,BENGIO Y. Equilibrated adaptive learning rates for non-convex optimization[C]//Advances in Neural Information Processing Systems 28:Annual Conference on Neural Information Processing Systems,2015:1504-1512.

Memo

Memo:
-
Last Update: 1900-01-01