Junhee Lee, Flavius Frasincar, Maria Mihaela Truşcă
{"title":"面向跨领域基于方面的情感分类的领域独立词选择器","authors":"Junhee Lee, Flavius Frasincar, Maria Mihaela Truşcă","doi":"10.1145/3626307.3626309","DOIUrl":null,"url":null,"abstract":"The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment Classification\",\"authors\":\"Junhee Lee, Flavius Frasincar, Maria Mihaela Truşcă\",\"doi\":\"10.1145/3626307.3626309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.\",\"PeriodicalId\":42971,\"journal\":{\"name\":\"Applied Computing Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computing Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3626307.3626309\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3626307.3626309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment Classification
The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.