半监督卷积神经网络的词义消歧

张春祥; 唐利波; 高雪瑶

doi:10.3969/j.issn.0258-2724.20200105

半监督卷积神经网络的词义消歧

doi: 10.3969/j.issn.0258-2724.20200105

哈尔滨理工大学计算机科学与技术学院, 黑龙江哈尔滨 150080

基金项目: 国家自然科学基金(61502124, 60903082)；中国博士后科学基金(2014M560249)；黑龙江省自然科学基金(F2015041, F201420)；黑龙江省普通高校基本科研业务费专项资金资助(LGYC2018JC014)

详细信息

作者简介:
张春祥（1974—），男，教授，博士，研究方向为自然语言处理与计算机图形学，E-mail：z6c6x666@163.com

通讯作者:
高雪瑶（1979—），女，教授，博士，研究方向为计算机图形学与自然语言处理，E-mail：xueyao_gao@163.com

中图分类号: TP391.2
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- 文章访问数: 298
- HTML全文浏览量: 192
- PDF下载量: 19
- 被引次数: 0
出版历程
- 收稿日期: 2020-03-05
- 修回日期: 2020-08-09
- 网络出版日期: 2021-11-13
- 刊出日期: 2020-09-15

Word Sense Disambiguation Based on Semi-Supervised Convolutional Neural Networks

School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China

摘要

摘要:
为了解决有标签语料获取困难的问题，提出了一种半监督学习的卷积神经网络(convolutional neural networks, CNN)汉语词义消歧方法. 首先，提取歧义词左右各2个词汇单元的词形、词性和语义类作为消歧特征，利用词向量工具将消歧特征向量化；然后，对有标签语料进行预处理，获取初始化聚类中心和阈值，同时，使用有标签语料对卷积神经网络消歧模型进行训练，利用优化后的卷积神经网络对无标签语料进行语义分类，选取满足阈值条件的高置信度语料添加到训练语料之中，不断重复上述过程，直到训练语料不再扩大为止；最后，使用SemEval-2007：Task#5作为有标签语料，使用哈尔滨工业大学无标注语料作为无标签语料进行实验. 实验结果表明：所提出方法使CNN的消歧准确率提高了3.1%.
- 半监督学习 /
- 卷积神经网络 /
- 词义消歧 /
- 消歧特征 /
- 词向量工具
Abstract:
In order to solve the difficulty of acquiring tagged corpus, a Chinese word sense disambiguation method is proposed on the basis of semi-supervised learning convolutional neural networks (CNN). Firstly, the word, part of speech and semantic category are extracted as discriminative features, which are acquired from 2 word units on the both left and right adjacent to ambiguous word. Word vector tool is used to denote discriminative features as vector. Secondly, tagged corpus is preprocessed to obtain initialized clustering centers and thresholds. At the same time, it is used to train convolutional neural networks. The optimized CNN is applied for determining the semantic categories of ambiguous words in the untagged corpus. Corpus with high confidence that meets threshold conditions is selected into the training corpus. The above process is repeated until the training corpus is no longer expanded. In the last, SemEval-2007: Task#5 is used as the tagged corpus, and the unannotated corpus from Harbin Institute of Technology is used as the untagged corpus. Experimental results show that the proposed method improve disambiguation accuracy of CNN by 3.1%.
- semi-supervised learning /
- convolutional neural networks /
- word sense disambiguation /
- discriminative features /
- word vector tool

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图 1 特征提取

Figure 1. Feature extraction

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图 2 特征矩阵构建过程

Figure 2. Construction process of feature matrix

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图 3 softmax层

Figure 3. softmax layer

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图 4 不同阈值和类别数下的平均消歧准确率

Figure 4. Average disambiguation accuracy at different thresholds and category numbers

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图 5 不同比例和类别数下的平均消歧准确率

Figure 5. Average disambiguation accuracy at different ratios and category numbers

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表 1 不同阈值的平均消歧准确率

Table 1. Average disambiguation accuracy of different thresholds %

类别数/类	歧义词汇	T = T_max	T = T_min	T = T_med	T = T_avg
2	表面菜单位动摇儿女镜头开通气息气象使眼光	88.3 88.9 77.8 86.7 90.2 50.0 60.5 64.2 93.8 70.2 76.9	82.4 72.2 94.4 80.0 86.3 57.1 58.8 66.5 87.5 72.5 84.6	76.4 88.9 94.4 93.3 92.4 50.0 58.8 68.6 87.5 76.3 76.9	82.4 88.9 94.4 73.3 96.3 50.7 58.8 67.3 93.8 79.6 84.6
3	补成立赶旗帜日子长城	62.0 84.6 66.7 50.0 51.6 68.0	61.9 88.2 66.7 62.5 48.4 68.0	52.4 80.8 55.6 50.0 48.4 68.0	71.4 76.9 77.8 68.8 48.4 80.0
4	吃动叫	56.0 66.7 61.5	56.0 61.1 64.1	56.0 61.1 64.1	56.0 61.1 53.8
平均准确率		70.7	71.0	70.0	73.2

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表 2 不同比率下的平均消歧准确率

Table 2. Average disambiguation accuracy of different rates %

类别数/类	歧义词汇	r = 1	r = 5	r = 10	r = 50	r = 100
2	表面菜单位动摇儿女镜头开通气息气象使眼光	82.4 88.9 94.5 86.7 82.2 64.3 58.8 64.2 93.8 72.5 84.6	82.4 77.8 94.5 80.0 88.9 50.0 58.8 68.6 87.5 72.5 76.9	76.0 83.3 88.9 80.0 84.6 57.1 58.8 66.5 87.5 70.2 76.9	82.4 88.9 83.3 80.0 90.7 50.0 58.8 66.5 87.5 74.9 84.6	88.3 88.9 94.5 86.7 92.4 57.1 58.8 68.0 93.8 75.4 88.0
3	补成立赶旗帜日子长城	71.4 76.9 72.2 60.5 48.4 60.0	61.9 84.6 61.1 56.3 48.4 68.0	52.4 76.9 72.2 56.3 51.6 68.0	61.9 76.9 61.1 62.5 48.4 68.0	71.4 80.8 66.7 62.5 51.6 64.0
4	吃动叫	60.0 55.6 66.7	64.0 72.2 64.1	60.0 67.1 61.5	60.0 50.0 71.8	64.0 66.7 64.0
平均准确率		72.2	70.9	69.8	70.4	74.2

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表 3 3 组实验的平均消歧准确率

Table 3. Average disambiguation accuracy of three groups of experiments %

类别数/类	歧义词汇	DBN	CNN	本文方法
2	表面菜单位动摇儿女镜头开通气息气象使眼光	61.1 55.6 58.8 62.5 70.0 53.3 70.0 71.4 62.5 62.5 71.4	82.3 72.2 82.3 93.7 94.9 53.3 85.0 64.2 87.5 81.2 71.4	82.4 88.9 94.4 73.3 96.3 50.7 58.8 67.3 93.8 79.6 84.6
3	补成立赶旗帜日子长城	50.0 63.3 50.0 55.6 46.9 38.1	64.9 66.6 55.5 72.2 50.0 71.4	71.4 76.9 77.8 68.8 48.4 80.0
4	吃动叫	43.5 50.0 30.0	52.1 50.0 70.0	56.0 61.1 53.8
平均准确率		56.3	71.0	73.2

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