{"title":"基于跨度的语义句法二元增强,用于方面情感三元组提取","authors":"Shuxia Ren, Zewei Guo, Xiaohan Li, Ruikun Zhong","doi":"10.1007/s10844-024-00881-w","DOIUrl":null,"url":null,"abstract":"<p>Aspect-Based Sentiment Triple Extraction (ASTE), a critical sub-task of Aspect-Based Sentiment Analysis (ABSA), has received extensive attention in recent years. ASTE aims to extract structured sentiment triples from texts, with most existing studies focusing on designing new strategic frameworks. Nonetheless, these methods often overlook the complex characteristics of linguistic expression and the deeper semantic nuances, leading to deficiencies in extracting the semantic representations of triples and effectively utilizing syntactic relationships in texts. To address these challenges, this paper introduces a span-based semantic and syntactic Dual-Enhanced model that deeply integrates rich syntactic information, such as part-of-speech tagging, constituent syntax, and dependency syntax structures. Specifically, we designed a semantic encoder and a syntactic encoder to capture the semantic-syntactic information closely related to the sentence’s underlying intent. Through a Feature Interaction Module, we effectively integrate information across different dimensions and promote a more comprehensive understanding of the relationships between aspects and opinions. We also adopted a span-based tagging scheme that generates more precise aspect sentiment triple extractions by exploring cross-level information and constraints. Experimental results on benchmark datasets derived from the SemEval challenge prove that our model significantly outperforms existing baselines.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"26 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Span-based semantic syntactic dual enhancement for aspect sentiment triplet extraction\",\"authors\":\"Shuxia Ren, Zewei Guo, Xiaohan Li, Ruikun Zhong\",\"doi\":\"10.1007/s10844-024-00881-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Aspect-Based Sentiment Triple Extraction (ASTE), a critical sub-task of Aspect-Based Sentiment Analysis (ABSA), has received extensive attention in recent years. ASTE aims to extract structured sentiment triples from texts, with most existing studies focusing on designing new strategic frameworks. Nonetheless, these methods often overlook the complex characteristics of linguistic expression and the deeper semantic nuances, leading to deficiencies in extracting the semantic representations of triples and effectively utilizing syntactic relationships in texts. To address these challenges, this paper introduces a span-based semantic and syntactic Dual-Enhanced model that deeply integrates rich syntactic information, such as part-of-speech tagging, constituent syntax, and dependency syntax structures. Specifically, we designed a semantic encoder and a syntactic encoder to capture the semantic-syntactic information closely related to the sentence’s underlying intent. Through a Feature Interaction Module, we effectively integrate information across different dimensions and promote a more comprehensive understanding of the relationships between aspects and opinions. We also adopted a span-based tagging scheme that generates more precise aspect sentiment triple extractions by exploring cross-level information and constraints. Experimental results on benchmark datasets derived from the SemEval challenge prove that our model significantly outperforms existing baselines.</p>\",\"PeriodicalId\":56119,\"journal\":{\"name\":\"Journal of Intelligent Information Systems\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10844-024-00881-w\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-024-00881-w","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Span-based semantic syntactic dual enhancement for aspect sentiment triplet extraction
Aspect-Based Sentiment Triple Extraction (ASTE), a critical sub-task of Aspect-Based Sentiment Analysis (ABSA), has received extensive attention in recent years. ASTE aims to extract structured sentiment triples from texts, with most existing studies focusing on designing new strategic frameworks. Nonetheless, these methods often overlook the complex characteristics of linguistic expression and the deeper semantic nuances, leading to deficiencies in extracting the semantic representations of triples and effectively utilizing syntactic relationships in texts. To address these challenges, this paper introduces a span-based semantic and syntactic Dual-Enhanced model that deeply integrates rich syntactic information, such as part-of-speech tagging, constituent syntax, and dependency syntax structures. Specifically, we designed a semantic encoder and a syntactic encoder to capture the semantic-syntactic information closely related to the sentence’s underlying intent. Through a Feature Interaction Module, we effectively integrate information across different dimensions and promote a more comprehensive understanding of the relationships between aspects and opinions. We also adopted a span-based tagging scheme that generates more precise aspect sentiment triple extractions by exploring cross-level information and constraints. Experimental results on benchmark datasets derived from the SemEval challenge prove that our model significantly outperforms existing baselines.
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.