[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81487-en":3,"doc-seo-81487-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81487,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Relation Extraction Model Based on Semantic Enhancement Mechanism","Relation extraction is a core natural language processing task for identifying entity-to-entity relationships as triplets in unstructured text, supporting downstream understanding, retrieval, and question answering. Many existing methods fail to adequately address triple overlap, where multiple relation triples share entities within the same sentence. The CasAug model, built on the CasRel framework, introduces a semantic enhancement mechanism that pre-classifies possible subjects, uses a subject lexicon to compute semantic similarity, applies attention to word-level relations, and weights enhanced semantics to produce final triplet extraction. Experiments show improved performance, better multi-relation extraction, and reduced redundant judgments.","Relation Extraction Model Based on Semantic Enhancement  \nMechanism  \nPeiyu Liu  \nSchool of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications Beijing, China  \nJunping Du∗ [junpingdu@126.com](junpingdu@126.com)  \nBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications  \nBeijing, China  \nYingxia Shao  \nBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications  \nBeijing, China  \nZeli Guan  \nBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications  \nBeijing, China  \narXiv :2311 .02564v2 [ cs .CL] 10 Jul 2026  \nAbstract  \nRelational extraction is one of the basic tasks related to information extraction in the field of natural language processing, and is an important link and core task in the fields of information extraction, natural language understanding, and information retrieval. None of the existing relation extraction methods can effectively solve the problem of triple overlap. The CasAug model proposed in this paper based on the CasRel framework combined with the semantic enhancement mechanism can solve this problem to a certain extent. The CasAug model enhances the semantics of the identified possible subjects by adding a semantic enhancement mechanism. First, based on the semantic coding of possible subjects, pre-classify the possible subjects, and then combine the subject lexicon to calculate the semantic similarity to obtain the similar vocabulary of possible subjects. According to the similar vocabulary obtained, each word in different relations is calculated through the attention mechanism. For the contribution of the possible subject, finally combine the relationship pre-classification results to weight the enhanced semantics of each relationship to find the enhanced semantics of the possible subject, and send the enhanced semantics combined with the possible subject to the object and relationship extraction module. Complete the final relation triplet extraction. The experimental results show that, compared with the baseline model, the CasAug model proposed in this paper has improved the effect of relation extraction, and CasAug’s ability to deal with overlapping problems and extract multiple relations is also better than the baseline model, indicating that the semantic enhancement mechanism proposed in this paper can further reduce the judgment of redundant relations and alleviate the problem of triple overlap.  \nKeywords  \nRelation extraction, semantic enhancement, attention mechanism, semantic similarity, subject lexicon  \n∗ Corresponding author.  \nThis work was supported by the Program of the National Natural Science Foundation of China (62192784, U22B2038, 62172056) .  \n1 Introduction  \nRelation extraction is one of the basic tasks related to information extraction in the field of natural language processing, which is mainly to identify the relationship between entities in text, and isone of the essential steps in downstream natural language processing tasks such as text comprehension and question answering. Relationship extraction is the extraction of entity relationship triplets from unstructured text to represent the relationships between two entities.  \nWith the development and progress of technology, there are three main extraction methods in the field of relationship extraction: relationship extraction based on supervised learning, relationship extraction based on semi-supervised learning, and relationship extraction based on distant supervised learning. Due to the fact that the dataset used for the latter two relationship extraction methods is a collection of weakly labeled samples, the dataset contains a large amount of noise. Therefore, how to effectively denoise the dataset is one of the difficulties that these two relat","cbCairx5m5cru8aC","https://ap.wps.com/l/cbCairx5m5cru8aC","pdf",581754,3,1,7,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the CasAug model aim to solve in relation extraction?\",\"answer\":\"It targets triple overlap, where different relation triples share the same entity and entities participate in multiple relational triples within a sentence.\"},{\"question\":\"How does CasAug enhance semantics for possible subjects?\",\"answer\":\"CasAug pre-classifies possible subjects using semantic coding, computes semantic similarity via a subject lexicon to get similar vocabulary, then uses an attention mechanism to calculate each word across different relations and weights enhanced semantics based on relation pre-classification.\"},{\"question\":\"What do the experimental results indicate compared with the baseline model?\",\"answer\":\"CasAug improves relation extraction effectiveness, handles overlapping problems better, and extracts multiple relations more effectively than the baseline model, helping reduce redundant relation 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problem does the CasAug model aim to solve in relation extraction?","Question",{"text":75,"@type":76},"It targets triple overlap, where different relation triples share the same entity and entities participate in multiple relational triples within a sentence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CasAug enhance semantics for possible subjects?",{"text":80,"@type":76},"CasAug pre-classifies possible subjects using semantic coding, computes semantic similarity via a subject lexicon to get similar vocabulary, then uses an attention mechanism to calculate each word across different relations and weights enhanced semantics based on relation pre-classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the experimental results indicate compared with the baseline model?",{"text":84,"@type":76},"CasAug improves relation extraction effectiveness, handles overlapping problems better, and extracts multiple relations more effectively than the baseline model, 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