[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-273644-105":59,"doc-detail-273644-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","synflow-a-multidimensional-diachronic-semantic-analysis-toolkit","SynFlow - A Multidimensional Diachronic Semantic Analysis Toolkit","","Lexical semantic change is often modeled with vector-space methods, which can obscure which usage aspects actually shift across time. SynFlow provides an open-source, multidimensional diachronic analysis toolkit that represents linguistically enriched word concordances as period-specific value distributions. It supports dependency co-occurrences, morphological features, constructional configurations, and semantic patterns via Frame Semantics, using configurable distance measures, statistical testing, value-level contribution scoring, and incremental clustering for lexical fillers.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/synflow-a-multidimensional-diachronic-semantic-analysis-toolkit/273644/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/synflow-a-multidimensional-diachronic-semantic-analysis-toolkit/273644.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does SynFlow address in lexical semantic change research?","Question",{"text":113,"@type":114},"It tackles the interpretability gap of vector-space approaches by focusing on which linguistic usage dimensions change over time and what drives those changes.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which linguistic dimensions can SynFlow analyze?",{"text":118,"@type":114},"SynFlow supports dependency-based co-occurrences, morphological features, constructional configurations, and can incorporate semantic patterns such as those from Frame Semantics through the same interface.",{"name":120,"@type":111,"acceptedAnswer":121},"How does SynFlow quantify semantic change across periods?",{"text":122,"@type":114},"It compares period-specific value distributions using distance measures like cosine distance, Jensen–Shannon divergence, or total variation distance, then decomposes changes into value-level contribution scores.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},273644,1790465340,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":46,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkit  \nBach Phan-Tat1 , Kris Heylen1,2 , Dirk Geeraerts1 , Stefano De Pascale1,3 , Dirk Speelman1  \n1Department of Linguistics, KU Leuven  \n2Instituut voor de Nederlandse Taal  \n3Vrije Universiteit Brussel  \nCorrespondence: [ttbach.phan@kuleuven.be](ttbach.phan@kuleuven.be)  \narXiv : 2608 . 19472v 3 [cs .CL] 29 Aug 2026  \nAbstract  \nLexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing. Diachronic corpus research instead examines interpretable dimensions such as syntactic behaviour, morphology, and constructional patterns, but typically through separate analytical workflows. We present SynFlow, an open-source toolkit for multidimensional diachronic analysis of linguistic usage. SynFlow represents linguistically enriched word concordances as period-specific distributions and applies a shared workflow across dependencybased co-occurrences, morphological features, constructional configurations, and semantic patterns such as those from Frame Semantics. It supports different distance measures, together with value-level contribution scores, statistical testing, and incremental clustering of lexical slot-fillers. We demonstrate SynFlow through a qualitative case study of the German adjective viral, showing how a single semantic development is reflected across syntactic, lexical, constructional, and morphological dimensions. We further report previously published resultson SemEval-2020 Task 1 to situate the performance of these representations relative to existing lexical semantic change detection systems.  \n1 Introduction  \nRecent computational work on lexical semantic change (LSC) is dominated by vector-space modelling approaches that compare vector representations of target words across historical periods (Periti and Montanelli, 2024 ; Tahmasebi et al., 2021 ; Tahmasebi and Dubossarsky, 2023) . While effective on shared tasks, such methods are difficult to interpret (Lenci et al., 2022), making their outputs difficult to relate directly to linguistic theories of semantic change: a model may indicate that a word has changed without directly revealing which aspects of its usage account for that change.  \nWe introduce SynFlow, an open-source toolkit for multidimensional diachronic semantic analysis. SynFlow represents each dimension as a periodspecific distribution of observable values and quantifies semantic change as differences between the diachronic distributions within a dimension. It natively supports automatic extraction and quantification of dependency-based co-occurrences, morphological features, and constructional configurations, while other semantic patterns such as those from Frame Semantics can be incorporated through the same interface. This allows different dimensions tobe analysed within a common diachronic workflow rather than requiring a separate analytical procedure for each representation.  \nSynFlow compares these distributions across periods using cosine distance, Jensen–Shannon divergence (JSD) (Menéndez et al., 1997), or total variation distance (TVD), and decomposes change scores into contributions from individual values, making it possible to determine which dimensions change and what drives the change. For lexical fillers, it additionally provides incremental clustering to trace broader thematic developments. The system therefore preserves a direct connection between quantitative change signals and the linguistic evidence underlying them.  \nThe main contribution of SynFlow is a shared workflow for multidimensional diachronic semantic analysis, providing temporal comparison, statistical testing, and value-level contribution scores.  \nWe demonstrate 1 SynFlow with a qualitative case study of the German adjective viral, showing how one semantic development is reflected across multiple linguistic dimensions. Previously published ","cbCairAnY3nZjFDc","https://ap.wps.com/l/cbCairAnY3nZjFDc","pdf",437369,"English","# Abstract\n# 1 Introduction\n# 2 Related work\n## 2.1 Linguistic dimensions in diachronic analysis\n## 2.2 Computational tools for linguistic profiling","[{\"question\":\"What problem does SynFlow address in lexical semantic change research?\",\"answer\":\"It tackles the interpretability gap of vector-space approaches by focusing on which linguistic usage dimensions change over time and what drives those changes.\"},{\"question\":\"Which linguistic dimensions can SynFlow analyze?\",\"answer\":\"SynFlow supports dependency-based co-occurrences, morphological features, constructional configurations, and can incorporate semantic patterns such as those from Frame Semantics through the same interface.\"},{\"question\":\"How does SynFlow quantify semantic change across periods?\",\"answer\":\"It compares period-specific value distributions using distance measures like cosine distance, Jensen–Shannon divergence, or total variation distance, then decomposes changes into value-level contribution scores.\"}]","SynFlow - A Multidimensional Diachronic Semantic Analysis Toolkit | PDF",1789472351]