[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-140661-105":59,"doc-detail-140661-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","bridging-text-embeddings-for-unconventional-linguistic-contexts-original-article","Bridging Text Embeddings for Unconventional Linguistic Contexts - Original Article","","This paper explores an extension of text embedding methods for extracting contextual information from atypical, symbolic textual sources. Using a corpus derived from the TV Tropes dataset, it proposes two models: an N-grams permutation approach and a database-like reinforcement methodology. Comparative evaluation shows that embeddings trained on the synthetic corpus improve accuracy by up to 45.2% relative to human-curated linguistic representations for a specialized Natural Language Processing setting.",{"@graph":69,"@context":122},[70,84,105],{"@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/bridging-text-embeddings-for-unconventional-linguistic-contexts-original-article/140661/",{"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/bridging-text-embeddings-for-unconventional-linguistic-contexts-original-article/140661.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-12","2026-08-24",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address?","Question",{"text":112,"@type":113},"The paper addresses how to learn contextual embeddings when the data lacks conventional linguistic structure, such as narrative tropes represented as symbolic associations rather than sentences.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which two models does the paper introduce?",{"text":117,"@type":113},"It introduces an N-grams permutation approach and a database-like reinforcement methodology that preserves co-occurrence patterns from the TV Tropes knowledge graph.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance improvement does the evaluation report?",{"text":121,"@type":113},"The evaluation reports that processing the artificially generated corpus with the proposed models improves accuracy by up to 45.2% compared with human-curated linguistic representations for this specialized NLP use case.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},140661,1787594516,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Original Article  \nBridging Text Embeddings for Unconventional Linguistic  \nContexts  \nPramath Parashar  \nData Science Specialist BHP Minerals Service Company.  \nReceived On: 23/11/2025 Revised On: 26/12/2025 Accepted On: 02/01/2026 Published On: 14/01/2026  \nAbstract - This document explores a novel extension of word embedding techniques to facilitate contextual information extraction from atypical textual sources. Utilizing a corpus derived from the tvtropes dataset, this work introduces two distinct models: an N-Grams Permutation approach and a Database-Like Reinforcement methodology. A comparative evaluation demonstrates that the artificially generated corpus, when processed by these models, significantly enhances accuracy by up to 45.2% over human-curated linguistic representations for this specialized application of Natural Language Processing.  \nKeywords-Text Embeddings, Word2vec, Symbolic Knowledge Representation, Synthetic Corpus Generation, Narrative Tropes, TV Tropes, Clustering Analysis, Skip-Gram Model, Semantic Similarity, Non-Linguistic Text Processing.  \n1. Introduction and Motivation  \nOver the past decade, text embeddings have emerged asa cornerstone of natural language processing, enabling machines to represent words as dense vectors that encode semantic, syntactic, and contextual information. From sentiment analysis to machine translation, these representations have transformed how we build NLP systems [1, 2] . Yet for all their success, embedding models typically rely on traditional text sources Wikipedia, news archives, scholarly articles where language follows conventional grammatical patterns and formal structures. Creative domains like fiction writing, game design, and screenwriting present a differ- ent challenge. These fields work with symbolic concepts recurring narrative patterns, character archetypes, plot devices that don’t naturally fit into sentence structures. The language of storytelling operates ata higher level of abstraction, where meaning emerges from patterns and associations rather than grammatical sequences. This creates a signif- icant gap when we try to apply standard embedding techniques to creative content.  \nConsider TV Tropes, a collaborative encyclopedia that catalogs narrative conven- tions across media. The platform documents thousands of recurring storytelling pat- terns what it calls ‖tropes‖ linked to specific works, genres, and characters. Un- like conventional text corpora, this knowledge exists as a web of abstract associations. There are no sentences to parse, no grammar to follow. The structure is entirely rela- tional: tropes connect to works, works share tropes, and meaning emerges from these co-occurrence patterns. This research asks: can we learn meaningful embeddings from symbolic data that lacks linguistic structure? To explore this question, we construct artificial corpora that simulate contextual relationships between tropes. Two approaches  \nare tested: an N-gram permutation method that randomly samples tropes associated with each work, and a databasedriven method that preserves the actual co-occurrence patterns from the TV Tropes knowledge graph.  \nOur central hypothesis is straightforward: even without grammar or natural sentence structure, welldesigned co-occurrence patterns can support meaningful embeddings. If the distributional relationships are rich enough, standard embedding algorithms should extract coherent semantic spaces. We test this through clustering analysis and vector arithmetic, examining whether the learned representations capture interpretable narrative concepts. The foundations for this work come from several research directions. Early work on distributional semantics showed that words with similar contexts tend to have similar meanings [3, 4] . This principle has proven surprisingly generalizable—researchers have successfully applied embedding techniques to product recommendations, biological se-quences, and even gameplay patterns [5, 6] ","cbCaioxXLk4qSPtN","https://ap.wps.com/l/cbCaioxXLk4qSPtN","pdf",240945,"English","# Introduction and Motivation\n# Related Work and Conceptual Foundations\n## Research Question and Motivation\n## Corpus Construction Approaches\n# Related Work and Conceptual Foundations\n## Distributional Semantics and Embeddings","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses how to learn contextual embeddings when the data lacks conventional linguistic structure, such as narrative tropes represented as symbolic associations rather than sentences.\"},{\"question\":\"Which two models does the paper introduce?\",\"answer\":\"It introduces an N-grams permutation approach and a database-like reinforcement methodology that preserves co-occurrence patterns from the TV Tropes knowledge graph.\"},{\"question\":\"What performance improvement does the evaluation report?\",\"answer\":\"The evaluation reports that processing the artificially generated corpus with the proposed models improves accuracy by up to 45.2% compared with human-curated linguistic representations for this specialized NLP use case.\"}]","Bridging Text Embeddings for Unconventional Linguistic Contexts - Original Article | PDF",13]