Aspect-oriented fine-grained opinion tuple extraction with adaptive span features

doi:10.11772/j.issn.1001-9081.2022040502

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1454-1460.DOI: 10.11772/j.issn.1001-9081.2022040502

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Aspect-oriented fine-grained opinion tuple extraction with adaptive span features

Linying CHEN¹^,², Jianhua LIU¹^,²(), Shuihua SUN¹^,², Zhixiong ZHENG¹^,², Honghui LIN¹^,², Jie LIN¹^,²

^1.College of Information Science and Engineering，Fujian University of Technology，Fuzhou Fujian 350118，China
^2.Fujian Provincial Key Laboratory of Big Data Mining and Applications （Fujian University of Technology），Fuzhou Fujian 350118，China

Received:2022-04-19 Revised:2022-06-06 Accepted:2022-06-09 Online:2022-07-01 Published:2023-05-10
Contact: Jianhua LIU
About author:CHEN Linying， born in 1999， M. S. candidate. Her research interests include natural language processing.
LIU Jianhua， born in 1967， Ph. D.， professor. His research interests include intelligent computing， machine learning.
SUN Shuihua， born in 1962， Ph. D.， professor. Her research interests include natural language processing， machine translation.
ZHENG Zhixiong， born in 1996， M. S. candidate. His research interests include natural language processing.
LIN Honghui， born in 1996， M. S. candidate. His research interests include natural language processing.
LIN Jie， born in 1999， M. S. candidate. His research interests include natural language processing.
Supported by:
National Natural Science Foundation of China(62172095);Fujian Provincial Natural Science Foundation(2019J01061137);Fuzhou Science and Technology Innovation Platform Program(2021-P-052)

面向方面的自适应跨度特征的细粒度意见元组提取

陈林颖¹^,², 刘建华¹^,²(), 孙水华¹^,², 郑智雄¹^,², 林鸿辉¹^,², 林杰¹^,²

^1.福建工程学院计算机科学与数学学院，福州 350118
^2.福建省大数据挖掘与应用技术重点实验室（福建工程学院），福州 350118

通讯作者: 刘建华
作者简介:陈林颖（1999—），女，福建莆田人，硕士研究生，CCF会员，主要研究方向：自然语言处理
刘建华（1967—），男，江西吉安人，教授，博士，CCF会员，主要研究方向：智能计算、机器学习 656095080@qq.com
孙水华（1962—），女，福建宁德人，教授，博士，CCF会员，主要研究方向：自然语言处理、机器翻译
郑智雄（1996—），男，福建莆田人，硕士研究生，CCF会员，主要研究方向：自然语言处理
林鸿辉（1996—），男，福建福州人，硕士研究生，CCF会员，主要研究方向：自然语言处理
林杰（1999—），男，福建宁德人，硕士研究生，主要研究方向：自然语言处理。
基金资助:
国家自然科学基金资助项目(62172095);福建省自然科学基金资助项目(2019J01061137);福州市科技创新平台项目(2021?P?052)

Abstract

Abstract:

Aspect-oriented Fine-grained Opinion Extraction （AFOE） extracts aspect terms and opinion terms from reviews in the form of opinion pairs or additionally extracts sentiment polarities of aspect terms on the basis of the above to form opinion triplets. Aiming at the problem of neglecting correlation between the opinion pairs and contexts， an aspect-oriented Adaptive Span Feature-Grid Tagging Scheme （ASF-GTS） model was proposed. Firstly， BERT （Bidirectional Encode Representation from Transformers） model was used to obtain the feature representation of the sentence. Then， the correlation between the opinion pair and local context was enhanced by the Adaptive Span Feature （ASF） method. Next， Opinion Pair Extraction （OPE） was transformed into a uniform grid tagging task by Grid Tagging Scheme （GTS）. Finally， the corresponding opinion pairs or opinion triplet were generated by the specific decoding strategy. Experiments were carried out on four AFOE benchmark datasets adaptive to the task of opinion tuple extraction. The results show that compared with GTS-BERT （Grid Tagging Scheme-BERT） model， the proposed model has the F1-score improved by 2.42% to 7.30% and 2.62% to 6.61% on opinion pair or opinion triplet tasks， respectively. The proposed model can effectively reserve the sentiment correlation between opinion pair and context， and extract opinion pairs and their sentiment polarities more accurately.

Key words: Grid Tagging Scheme (GTS), aspect term, opinion term, Opinion Pair Extraction (OPE), Opinion Triplet Extraction (OTE), Aspect-oriented Fine-grained Opinion Extraction (AFOE)

摘要：

面向方面的细粒度意见提取（AFOE）以意见对的形式从评论中提取方面词和意见词，或在此基础上再提取方面词的情感极性形成意见三元组。针对现有研究方法忽略了意见对与上下文相关性的问题，提出一种面向方面的自适应跨度特征的网格标记方案（ASF-GTS）模型。首先，利用BERT（Bidirectional Encode Representation from Transformers）模型获得句子的特征表示；然后，采用自适应跨度特征（ASF）方法加强意见对与局部上下文的联系；其次，通过网格标记方案（GTS）将意见对提取（OPE）转化为统一的网格标记任务；最后，使用特定的解码策略生成对应的意见对或意见三元组。在适用于意见元组提取任务的四个AFOE基准数据集上进行实验，结果表明，与GTS-BERT（Grid Tagging Scheme-BERT）模型相比，所提模型在意见对和意见三元组任务上的F1值分别提高了2.42%~7.30%和2.62%~6.61%。所提模型能够有效保留意见对与上下文的情感联系，更精确地提取意见对及其情感极性。

关键词: 网格标记方案, 方面词, 意见词, 意见对提取, 意见三元组提取, 面向方面的细粒度意见提取

CLC Number:

TP391.1

Linying CHEN, Jianhua LIU, Shuihua SUN, Zhixiong ZHENG, Honghui LIN, Jie LIN. Aspect-oriented fine-grained opinion tuple extraction with adaptive span features[J]. Journal of Computer Applications, 2023, 43(5): 1454-1460.

陈林颖, 刘建华, 孙水华, 郑智雄, 林鸿辉, 林杰. 面向方面的自适应跨度特征的细粒度意见元组提取[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1454-1460.

Figures/Tables 8

References 21

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数据集	数据划分	Sen	Asp	Opi	Pai	Tri
14Res	训练集	1 259	2 064	2 098	2 356	2 356
	验证集	315	487	506	580	580
	测试集	493	851	866	1 008	1 008
14Lap	训练集	899	1 257	1 270	1 452	1 452
	验证集	225	332	313	383	383
	测试集	332	467	478	547	547
15Res	训练集	603	871	966	1 038	1 038
	验证集	151	205	226	239	239
	测试集	325	436	469	493	493
16Res	训练集	863	1 213	1 329	1 421	1 421
	验证集	216	298	331	348	348
	测试集	328	456	485	525	525

数据集	数据划分	Sen	Asp	Opi	Pai	Tri
14Res	训练集	1 259	2 064	2 098	2 356	2 356
	验证集	315	487	506	580	580
	测试集	493	851	866	1 008	1 008
14Lap	训练集	899	1 257	1 270	1 452	1 452
	验证集	225	332	313	383	383
	测试集	332	467	478	547	547
15Res	训练集	603	871	966	1 038	1 038
	验证集	151	205	226	239	239
	测试集	325	436	469	493	493
16Res	训练集	863	1 213	1 329	1 421	1 421
	验证集	216	298	331	348	348
	测试集	328	456	485	525	525

提取方式	模型	14Res			14Lap			15Res			16Res
提取方式	模型	P	R	F₁	P	R	F₁	P	R	F₁	P	R	F₁
管道方式	BiLSTM-ATT+IOG^［17］	69.99	61.58	65.46	64.93	44.56	52.84	59.14	56.38	57.73	66.07	62.55	64.13
	DE-CNN+IOG^［19］	67.70	69.41	68.55	59.59	51.68	55.35	59.18	60.08	58.04	62.97	66.22	64.55
	RINANTE+IOG^［6］	70.16	65.47	67.74	61.76	53.11	57.10	63.24	55.57	59.16
统一提取	GTS-BERT^［1］	75.95	70.81	73.29	66.15	63.11	64.60	66.40	68.71	67.53	72.25	77.41	74.74
本文模型	ASF-GTS	81.86	75.66	78.64	72.01	64.22	67.90	78.79	63.80	70.51	75.78	77.33	76.55

提取方式	模型	14Res			14Lap			15Res			16Res
提取方式	模型	P	R	F₁	P	R	F₁	P	R	F₁	P	R	F₁
管道方式	BiLSTM-ATT+IOG^［17］	69.99	61.58	65.46	64.93	44.56	52.84	59.14	56.38	57.73	66.07	62.55	64.13
	DE-CNN+IOG^［19］	67.70	69.41	68.55	59.59	51.68	55.35	59.18	60.08	58.04	62.97	66.22	64.55
	RINANTE+IOG^［6］	70.16	65.47	67.74	61.76	53.11	57.10	63.24	55.57	59.16
统一提取	GTS-BERT^［1］	75.95	70.81	73.29	66.15	63.11	64.60	66.40	68.71	67.53	72.25	77.41	74.74
本文模型	ASF-GTS	81.86	75.66	78.64	72.01	64.22	67.90	78.79	63.80	70.51	75.78	77.33	76.55

模型	14Res			14Lap			15Res			16Res
模型	P	R	F₁	P	R	F₁	P	R	F₁	P	R	F₁
Peng-unified-R+IOG	58.89	60.41	59.64	48.62	45.52	47.02	51.70	46.04	48.71	59.25	58.09	58.67
IMN+IOG	59.57	63.88	61.65	49.21	46.23	47.68	55.24	52.33	53.75
GTS-BERT	70.92	69.49	70.20	57.52	51.91	54.58	59.29	58.07	58.67	63.95	70.85	67.22
ASF-GTS	75.62	70.81	73.13	60.66	53.76	56.91	65.19	60.12	62.55	67.03	71.04	68.98

Aspect-oriented fine-grained opinion tuple extraction with adaptive span features

面向方面的自适应跨度特征的细粒度意见元组提取

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Figures/Tables 8

References 21

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Metrics

模型	不同样本预测的结果
模型	样本1： The avocado salad is a personal fave.	样本2： Montparnasse's desserts — especially the silken creme brulee and paper — thin apple tart — are good enough on their own to make the restaurant worth the trip.	样本3： menu-uneventful， small
Ground Truth （GT）	（avocado salad-fave-positive）	（desserts-good-positive）（crème brulee-silken-positive）（apple tart-thin-positive）	（menu-uneventful-negative）（menu-small-negative）
GTS-BERT	（avocado salad-fave-positive） √	（apple tart-good-positive） × （desserts-good-positive） √ （crème brulee-good-positive） ×	（menu-uneventful-negative） × （menu-small-neutral） ×
GTS-BERT+CDM	（NULL-NULL-NULL）	（apple tart-good-positive） × （crème brulee-good-positive）×	（menu-uneventful-positive） ×
ASF-GTS	（avocado salad-fave-positive） √	（apple tart-good-positive）× （desserts-good-positive） √ （crème brulee-good-positive） ×	（menu-uneventful-negative） √ （menu-small-negative） √

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