基于知识增强的文本隐喻识别图编码方法

黄河燕; 刘啸; 刘茜

doi:10.7544/issn1000-1239.202110927

基于知识增强的文本隐喻识别图编码方法

北京理工大学计算机学院　北京　100081
北京海量语言信息处理与云计算工程研究中心　北京　100081
北京理工大学东南信息技术研究院　福建莆田　351100

基金项目: 国家重点研发计划项目（2018YFB1005100）；国家自然科学基金项目（61732005）

详细信息

作者简介:
黄河燕: 1963年生.博士，教授，博士生导师.CCF会员.主要研究方向为自然语言处理、信息抽取与信息检索

刘啸: 1994年生.博士研究生.主要研究方向为自然语言处理、信息抽取与事件抽取

刘茜: 1991年生.博士.主要研究方向为自然语言处理、信息检索与语义表示学习

中图分类号: TP391
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出版历程
- 收稿日期: 2021-09-08
- 修回日期: 2022-04-14
- 网络出版日期: 2023-02-10
- 刊出日期: 2022-12-31

Knowledge-Enhanced Graph Encoding Method for Metaphor Detection in Text

School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081
Beijing Engineering Research Center of High-Volume Language Information Processing and Cloud Computing Applications, Beijing 100081
Southeast Academy of Information Technology, Beijing Institute of Technology, Putian, Fujian 351100

Funds: This work was supported by the National Key Research and Development Program of China (2018YFB1005100) and the National Natural Science Foundation of China (61732005).

摘要

摘要:
隐喻识别是自然语言处理中语义理解的重要任务之一，目标为识别某一概念在使用时是否借用了其他概念的属性和特点. 由于单纯的神经网络方法受到数据集规模和标注稀疏性问题的制约，近年来，隐喻识别研究者开始探索如何利用其他任务中的知识和粗粒度句法知识结合神经网络模型，获得更有效的特征向量进行文本序列编码和建模.然而，现有方法忽略了词义项知识和细粒度句法知识，造成了外部知识利用率低的问题，难以建模复杂语境.针对上述问题，提出一种基于知识增强的图编码方法（knowledge-enhanced graph encoding method，KEG）来进行文本中的隐喻识别. 该方法分为3个部分：在文本编码层，利用词义项知识训练语义向量，与预训练模型产生的上下文向量结合，增强语义表示；在图网络层，利用细粒度句法知识构建信息图，进而计算细粒度上下文，结合图循环神经网络进行迭代式状态传递，获得表示词的节点向量和表示句子的全局向量，实现对复杂语境的高效建模；在解码层，按照序列标注架构，采用条件随机场对序列标签进行解码.实验结果表明，该方法的性能在4个国际公开数据集上均获得有效提升.
- 隐喻识别 /
- 图循环神经网络 /
- 知识增强方法 /
- 词义项知识 /
- 细类别句法知识 /
- 序列标注
Abstract:
Metaphor recognition is one of the essential tasks of semantic understanding in natural language processing, aiming to identify whether one concept is viewed in terms of the properties and characteristics of the other. Since pure neural network methods are restricted by the scale of datasets and the sparsity of human annotations, recent researchers working on metaphor recognition explore how to combine the knowledge in other tasks and coarse-grained syntactic knowledge with neural network models, obtaining more effective feature vectors for sequence coding and modeling in text. However, the existing methods ignore the word sense knowledge and fine-grained syntactic knowledge, resulting in the problem of low utilization of external knowledge and the difficulty to model complex context. Aiming at the above issues, a knowledge-enhanced graph encoding method (KEG) for metaphor detection in text is proposed. This method consists of three parts. In the encoding layer, the sense vector is trained using the word sense knowledge, combined with the context vector generated by the pre-training model to enhance the semantic representation. In the graph layer, the information graph is constructed using fine-grained syntactic knowledge, and then the fine-grained context is calculated. The layer is combined with the graph recurrent neural network, whose state transition is carried out iteratively to obtain the node vector and the global vector representing the word and the sentence, respectively, to realize the efficient modeling of the complex context. In the decoding layer, conditional random fields are used to decode the sequence tags following the sequence labeling architecture. Experimental results show that this method effectively improves the performance on four international public datasets.
- metaphor detection /
- graph recurrent neural network /
- knowledge-enhanced method /
- word sense knowledge /
- fine-grained syntactic knowledge /
- sequence labeling

HTML全文

图 1 KEG整体网络架构图

Figure 1. Overall network framework of KEG

下载: 全尺寸图片幻灯片

图 2 文本编码层结构

Figure 2. Structure of the encoding layer

下载: 全尺寸图片幻灯片

图 3 图循环神经网络层结构

Figure 3. Layer structure of the graph recurrent neural network

下载: 全尺寸图片幻灯片

表 1 包含隐喻词汇的文本样例

Table 1 Examples of Sentences with Metaphor Words

编号	文本样例
1	It went to his head rather.
2	How to kill a process?
3	I invested myself fully in this relationship.
4	They forged contacts with those left behind in Germany.
注：黑体单词表示隐喻现象.

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表 2 数据集统计信息

Table 2 Statistics of Datasets

数据集	单词总数	句子总数	隐喻总数	隐喻占比/%	平均单词数
VUA-VERB	239847	16189	6554	28.36	14.82
VUA-ALLPOS	239847	16189	15026	15.85	14.82
TroFi	105949	3737	1627	43.54	28.35
MOH-X	5178	647	315	48.68	8.00

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表 3 本文实验中使用的超参数

Table 3 Hyper-Parameters Used in Our Experiment

超参数	取值
${{\boldsymbol{e}}^{\rm{c}}}$ 的向量维度	1024
${{\boldsymbol{e}}^{\rm{s}}}$ 的向量维度	200
${\boldsymbol{k}}$ 的向量维度	256
${\boldsymbol{m}}$ ， ${\boldsymbol{h}}$ ， ${\boldsymbol{g}}$ 的向量维度	512
状态传递次数 $K$	3
批大小（batch size）	16
BERT模型dropout率	0.5
模型其他部分dropout率	0.4
BERT模型学习率	1E−5
模型其他部分学习率	1E−3
BERT模型 ${L_2}$ 权重衰减	1E−5
模型其他部分 ${L_2}$ 权重衰减	1E−3
Warmup迭代轮次	5
最大迭代轮次	50
梯度裁剪	4.0

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表 4 KEG与SOTA方法对比的实验结果

Table 4 Experimental Results of KEG and SOTA Methods %

组别	对比模型	VUA-VERB				VUA-ALLPOS				TroFi				MOH-X
组别	对比模型	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc
第1组	RNN_ELMo^[7]	68.2	71.3	69.7	81.4	71.6	73.6	72.6	93.1	70.7	71.6	71.1	74.6	79.1	73.5	75.6	77.2
	RNN_BERT^[20]	66.7	71.5	69.0	80.7	71.5	71.9	71.7	92.9	70.3	67.1	68.7	73.4	75.1	81.8	78.2	78.1
	RNN_HG^[6]	69.3	72.3	70.8	82.1	71.8	76.3	74.0	93.6	67.4	77.8	72.2	74.9	79.7	79.8	79.8	79.7
	RNN_MHCA^[6]	66.3	75.2	70.5	81.8	73.0	75.7	74.3	93.8	68.6	76.8	72.4	75.2	77.5	83.1	80.0	79.8
第2组	Go Figure!^[9]	73.2	82.3	77.5		72.1	74.8	73.4
第2组	DeepMet-S^[10]	76.2	78.3	77.2	86.2	73.8	73.2	73.5	90.5
第3组	GCN_BERT^[11]									72.3	70.5	70.7	72.0	79.8	79.4	79.3	79.4
第3组	MWE^[11]									73.8	71.8	72.8	73.5	80.0	80.4	80.2	80.5
本文	KEG	80.5	77.4	78.9	86.7	74.6	76.5	75.5	92.8	75.6	77.8	76.7	76.2	82.1	81.6	81.8	81.6

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表 5 对比不同状态传递次数的实验结果

Table 5 Experimental Results With Different State Transition Times %

状态传递次数K	VUA-VERB				VUA-ALLPOS				TroFi				MOH-X
状态传递次数K	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc
1	67.3	72.6	69.8	82.5	71.8	73.3	72.5	91.7	72.6	70.4	71.5	74.6	77.8	79.8	78.8	78.1
2	74.6	75.7	75.1	84.2	73.3	75.7	74.5	92.4	74.1	75.6	74.8	75.3	81.3	80.5	80.9	80.8
3	80.5	77.4	78.9	86.7	74.6	76.5	75.5	92.8	75.6	77.8	76.7	76.2	82.1	81.6	81.8	81.6
4	80.9	77.1	79.0	86.2	75.1	76.0	75.5	92.8	75.8	77.5	76.6	76.3	82.1	81.8	81.9	81.2
5	80.8	77.2	78.9	86.6	74.8	76.1	75.4	93.1	76.1	77.8	76.9	75.3	81.4	82.1	81.7	81.6

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表 6 词向量和上下文向量构建方法的实验结果

Table 6 Experimental Results of Constructing Word Vectors and Context Vectors %

对比模型	VUA-VERB				VUA-ALLPOS				TroFi				MOH-X
对比模型	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc
KEG-word2vec	74.2	73.2	73.7	82.3	73.5	74.7	74.1	90.6	74.1	73.5	73.8	74.4	78.5	76.6	77.5	78.6
KEG-GloVe	74.6	73.2	73.9	82.6	73.6	74.7	74.1	90.8	74.0	73.8	73.9	74.2	78.4	76.8	77.6	79.1
KEG-ELMo	76.4	75.5	75.9	84.8	74.2	76.1	75.1	91.7	74.6	75.8	75.2	75.6	80.4	80.3	80.3	80.7
KEG	80.5	77.4	78.9	86.7	74.6	76.5	75.5	92.8	75.6	77.8	76.7	76.2	82.1	81.6	81.8	81.6

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表 7 KEG有无词级别义项知识的实验结果

Table 7 Experimental Results of KEG With and Without Lexicon Word Sense Knowledge %

对比模型	VUA-VERB				VUA-ALLPOS				TroFi				MOH-X
对比模型	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc
KEG-NOS	68.2	72.9	70.5	82.7	72.6	73.1	72.8	92.1	73.2	71.5	72.3	75.4	79.4	78.2	78.8	78.8
KEG	80.5	77.4	78.9	86.7	74.6	76.5	75.5	92.8	75.6	77.8	76.7	76.2	82.1	81.6	81.8	81.6

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表 8 KEG有无细粒度句法知识的实验结果

Table 8 Experimental Results of KEG With and Without Fine-Grained Syntactic Knowledge %

对比模型	VUA-VERB				VUA-ALLPOS				TroFi				MOH-X
对比模型	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc
KEG-UCG	67.5	71.1	69.3	80.6	70.3	71.3	70.8	89.4	72.5	70.1	71.3	74.0	77.5	76.9	77.2	77.1
KEG-CSK	71.2	70.7	70.9	81.3	72.0	71.4	71.7	90.2	73.1	70.7	71.9	74.7	78.2	78.3	78.2	78.3
KEG	80.5	77.4	78.9	86.7	74.6	76.5	75.5	92.8	75.6	77.8	76.7	76.2	82.1	81.6	81.8	81.6

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表 9 2种解码方式的实验结果

Table 9 Experimental Results of Two Decoding Methods %

对比模型	VUA-VERB				VUA-ALLPOS				TroFi				MOH-X
对比模型	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc	P	R	F1	Acc
KEG-C	79.5	77.3	78.4	86.5	74.2	75.6	74.9	92.2	75.8	76.3	76.0	75.9	82.0	81.2	81.6	81.5
KEG	80.5	77.4	78.9	86.7	74.6	76.5	75.5	92.8	75.6	77.8	76.7	76.2	82.1	81.6	81.8	81.6

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