面向语法加权图文本的方面情感三元组抽取

韩虎; 孟甜甜

doi:10.13700/j.bh.1001-5965.2022.0443

面向语法加权图文本的方面情感三元组抽取

doi: 10.13700/j.bh.1001-5965.2022.0443

韩虎^,,
孟甜甜

兰州交通大学电子与信息工程学院，兰州 730070

基金项目: 国家自然科学基金(62166024)

详细信息

通讯作者:
E-mail：hanhu_lzjtu@mail.lzjtu.cn

中图分类号: TP391
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出版历程
- 收稿日期: 2022-05-31
- 录用日期: 2022-11-04
- 网络出版日期: 2023-01-09
- 整期出版日期: 2024-02-27

Aspect sentiment triple extraction for grammar-weighted graph text

HAN Hu^,,
MENG Tiantian

School of Electronic and Information Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China

Funds: National Natural Science Foundation of China (62166024)

More Information

Corresponding author: E-mail：hanhu_lzjtu@mail.lzjtu.cn

摘要

摘要:
方面情感三元组抽取包括方面抽取、意见抽取和方面情感分类3项任务，以管道方式解决该任务的研究方法无法利用元素之间的交互信息，同时也会造成错误传播和冗余训练。基于此，提出一种基于门控注意力和加权图文本的方面情感三元组抽取方法。采用双向长短时记忆网络学习句子的序列特征表示；利用门控注意力单元学习单词之间的线性联系；利用语法距离加权图卷积网络增强三元组元素之间的交互；利用网格标记推理策略预测三元组。在4个公开数据集上进行实验，结果表明：所提方法可以有效增强三元组元素之间的交互，提高三元组抽取的准确率；同时，所提方法的F₁值分别为57.94%、70.54%、61.95%和67.66%，与基准模型相比均有所提高。
- 三元组抽取 /
- 门控注意力 /
- 加权图文本 /
- 双向长短时记忆网络 /
- 网格标记
Abstract:
Aspect sentiment triple extraction includes three tasks: aspect term extraction, opinion term extraction, and aspect sentiment classification. However, research methods that solve this task in a pipeline way cannot utilize the interaction information between elements, and will also cause error propagation and redundant training. To solve the above problems, an aspect sentiment triple extraction method based on gated attention and weighted graph text is proposed, which makes full use of the semantic and grammatical relationships between triple elements to enhance element interaction. Firstly, the model uses a bidirectional long-short-term memory network to learn the sequence feature representation of sentences. Secondly, a gated attention unit is used to learn linear connections between words. Thirdly, a grammatical distance-weighted graph convolutional network is employed to enhance the interactions between triplet elements. Finally, a grid tagging inference strategy is applied to predict triples. Experimental results on four public datasets show that the proposed method can effectively enhance the interaction between triple elements and improve the accuracy of triple extraction. Moreover, the F₁ values of the proposed method are 57.94%, 70.54%, 61.95% and 67.66%, respectively, which are all improved compared to the baseline model.
- triple extraction /
- gated attention /
- weighted graph text /
- bidirectional long-short-term memory network /
- grid tagging

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图 1 GTS标记示例

Figure 1. GTS tagging example

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图 2 模型整体架构

Figure 2. Overall architecture of model

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图 3 门控注意力结构

Figure 3. Architecture of gated attention

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图 4 加权图文本架构

Figure 4. Architecture of weighted graph text

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图 5 方面“Foods”的依赖树语法距离

Figure 5. Syntax distance of dependence tree for aspect “Foods”

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图 6 方面“price”的依赖树语法距离

Figure 6. Syntax distance of dependence tree for aspect “price”

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图 7 GCN层数与F₁的关系

Figure 7. Relationship between number of GCN layers and F₁

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表 1 数据集统计结果

Table 1. Dataset statistics results

数据集		句子数量	三元组数量	情感极性积极的三元组数量	情感极性中立的三元组数量	情感极性消极的三元组数量
lap14	训练集	899	1452	808	111	533
	验证集	225	383	199	48	136
	测试集	332	547	364	67	116
res14	训练集	1259	2356	1693	172	491
	验证集	315	580	427	46	107
	测试集	493	1008	784	68	156
res15	训练集	603	1038	799	29	210
	验证集	151	239	181	9	49
	测试集	325	493	324	25	144
res16	训练集	863	1421	1036	55	330
	验证集	216	348	263	8	77
	测试集	328	525	416	30	79

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表 2 GA-DWGT模型与基准模型结果对比

Table 2. Comparison of GA-DWGT model and benchmark model results %

模型	P				R				F₁
模型	lap14	res14	res15	res16	lap14	res14	res15	res16	lap14	res14	res15	res16
Li-unified-R+PD	42.25	41.44	43.34	38.19	42.78	68.79	50.73	53.47	42.47	51.68	46.69	44.51
Peng-unified-R+PD	40.40	44.18	40.97	46.76	47.24	62.99	54.68	62.97	43.50	51.89	46.79	53.62
Peng-unified-R+IOG	48.62	58.89	51.70	59.25	45.52	60.41	46.04	58.09	47.02	59.64	48.71	58.67
IMN+IOG	49.21	59.57	55.24		46.23	63.88	52.33		47.68	61.65	53.75
PASTE	52.10	63.40	54.80	62.30	48.10	61.90	52.60	63.60	50.00	62.60	53.70	62.90
GTS-CNN	55.93	70.79	60.09	62.63	47.52	61.71	53.57	66.98	51.38	65.94	56.64	64.73
GTS-LSTM	59.42	67.28	63.26	66.07	45.13	61.91	50.71	65.05	51.30	64.49	56.29	65.56
GA-DWGT	59.09	70.67	65.87	67.80	47.34	62.42	50.92	65.39	52.60	66.21	57.44	66.29
PASTE-BERT	59.70	68.70	63.60	68.00	55.30	63.80	59.80	67.70	57.40	66.10	61.60	67.80
GTS-BERT	57.52	70.92	59.29	68.58	51.92	69.49	58.07	66.60	54.58	70.20	58.67	67.58
GA-DWGT-BERT	63.80	70.74	64.11	65.41	51.74	68.38	59.92	70.08	57.94	70.54	61.95	67.66

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表 3 对比模型的可训练参数数量

Table 3. Number of trainable parameters of comparison models

模型	可训练参数数量/10⁶
Li-unified-R+PD	0.31
Peng-unified-R+PD	0.21
Peng-unified-R+IOG	0.36
IMN+IOG	0.45
PASTE	0.71
GTS-CNN	0.47
GTS-LSTM	0.33
GA-DWGT	0.33

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表 4 消融实验结果

Table 4. Ablation study results %

模型	P				R				F₁
模型	lap14	res14	res15	res16	lap14	res14	res15	res16	lap14	res14	res15	res16
GA-DWGT-GAU-WG	52.29	67.75	58.79	66.46	43.85	59.79	49.89	61.19	47.71	63.18	53.98	63.72
GA-DWGT-WG	57.07	67.78	59.65	67.68	42.20	61.01	55.01	64.29	48.52	64.22	57.23	65.94
GA-DWGT-GAU	55.71	69.57	62.89	65.59	43.85	59.59	49.89	62.55	49.08	64.20	55.65	64.03
GA-DWGT	59.09	70.67	65.87	67.80	47.34	62.42	50.92	65.39	52.60	66.21	57.44	66.29

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表 5 案例分析

Table 5. Case study

例句	正确的三元组	模型预测三元组
例句	正确的三元组	GTS-CNN	GTS-LSTM	GA-DWGT
Made interneting difficult to maintain	(interneting, difficult, NEG)	(maintain, difficult, POS)	(maintain, difficult, POS)	(interneting, difficult, NEG)
Made interneting difficult to maintain and the screen is very sharp	(speed, much more, POS) (screen, sharp, POS)	(screen, sharp, POS)	(screen, sharp, POS)	(speed, much more, POS) (screen, sharp, POS)
The bread is top notch as well	(bread, top notch, POS)	(bread, top notch, POS)	(bread, top notch, POS)	(bread, top notch, POS)
The staff should be a bit more friendly	(staff, friendly, NEG)	(staff, more friendly, POS)	(staff, more friendly, POS) (staff, should be, POS)	(staff, friendly, NEG) (staff, should, NEG)
The food was extremely tasty, creatively presented and the wine excellent	(food, tasty, POS) (food, creatively presented, POS) (wine, excellent, POS)	(food, tasty, POS) (food, creatively presented, POS) (wine, excellent, POS)	(food, tasty, POS) (food, creatively presented, POS) (wine, excellent, POS) (food, excellent, POS)	(food, tasty, POS) (food, creatively presented, POS) (wine, excellent, POS)

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