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It presents Latent Relational Analysis (LRA), an unsupervised method extending the Vector Space Model to measure relational similarity by automatically derived connecting patterns, frequency smoothing via Singular Value Decomposition, and synonym-based transformations of word pairs. Experiments on 374 word-analogy questions show higher relational performance than the baseline VSM approach, with results comparable to average human scores.",{"@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/similarity-of-semantic-relations-latent-relational-analysis-lra-paper/267067/",{"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/similarity-of-semantic-relations-latent-relational-analysis-lra-paper/267067.png","ImageObject",300,407,{"name":92,"@type":93},"Mia  ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-14",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the difference between attributional similarity and relational similarity?","Question",{"text":113,"@type":114},"Attributional similarity measures correspondence between attributes, while relational similarity measures correspondence between relations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does Latent Relational Analysis (LRA) do differently from the Vector Space Model?",{"text":118,"@type":114},"LRA derives patterns automatically from a corpus, uses Singular Value Decomposition to smooth frequency data, and explores word-pair variations by synonym-based transformations.",{"name":120,"@type":111,"acceptedAnswer":121},"How well does LRA perform on word analogy questions compared with the VSM approach?",{"text":122,"@type":114},"LRA achieves 56% on the 374 analogy questions, which is stated as statistically equivalent to an average human score of 57%, improving over the VSM baseline reported as 47%.","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},267067,1789414373,{"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":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687207024478,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Similarity of Semantic Relations  \nPeter D. Turney 􀀀  \nNational Research Council Canada  \nThere are at least two kinds of semantic similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving ascore of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of prede􀀂ned patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.  \n1. Introduction  \nThere are at least two kinds of semantic similarity. Attributional similarity is correspondence between attributes and relational similarity is correspondence between relations (Medin, Goldstone, and Gentner, 1990) . When two words have a high degree of attributional similarity, we call them synonyms. When two word pairs have a high degree of relational similarity, we say they are analogous.  \nVerbal analogies are often written in the formA:B::C:D, meaning A is to B as C is to D; for example, traf􀀂c:street::water:riverbed. Traf􀀂c 􀀃ows over a street; water 􀀃ows over a riverbed. A street carries traf􀀂c; a riverbed carries water. There is a high degree of relational similarity between the word pair traf􀀂c:street and the word pair water:riverbed. In fact, this analogy is the basis of several mathematical theories of traf􀀂c 􀀃ow (Daganzo, 1994) .  \nIn Section 2, we look more closely at the connections between attributional and relational similarity. In analogies such as mason:stone::carpenter:wood, it seems that relational similarity can be reduced to attributional similarity, since mason and carpenter are attributionally similar, as are stone and wood. In general, this reduction fails. Consider the analogy traf􀀂c:street::water:riverbed. Traf􀀂c and water are not attributionally similar. Street and riverbed are only moderately attributionally similar.  \nMany algorithms have been proposed for measuring the attributional similarity between two words (Lesk, 1969; Resnik, 1995; Landauer and Dumais, 1997; Jiang and Conrath, 1997;  Lin, 1998b; Turney, 2001; Budanitsky and Hirst, 2001; Banerjee and Ped-  \n􀀀 Institute for Information Technology, National Research Council Canada, Ottawa, Canada, K1A 0R6  \n✁c National Research Council Canada  \nComputational Linguistics Volume 1, Number 1  \nersen, 2003) . Measures of attributional similarity have been studied extensively, due to their applications in problems such as recognizing synonyms (Landauer and Dumais, 1997), information retrieval (Deerwester et al., 1990), determining semantic orientation (Turney, 2002), grading student essays (Rehder et al., 1998), measuring textual cohesion (Morris and Hirst, 1991), and word sense disambiguation (Lesk, 1986) .  \nOn the other hand, since measures of relational similarity are not as well developed as measures of attribut","cbCaijAKAWVkJHvA","https://ap.wps.com/l/cbCaijAKAWVkJHvA","pdf",329472,37,"English","# Introduction\n## Semantic similarity types: attributional vs relational\n## Verbal analogies and relational structure\n## Motivation for measuring relational similarity\n## Vector Space Model and its adaptation\n## Latent Relational Analysis (LRA) method\n## Experimental evaluation","[{\"question\":\"What is the difference between attributional similarity and relational similarity?\",\"answer\":\"Attributional similarity measures correspondence between attributes, while relational similarity measures correspondence between relations.\"},{\"question\":\"What does Latent Relational Analysis (LRA) do differently from the Vector Space Model?\",\"answer\":\"LRA derives patterns automatically from a corpus, uses Singular Value Decomposition to smooth frequency data, and explores word-pair variations by synonym-based transformations.\"},{\"question\":\"How well does LRA perform on word analogy questions compared with the VSM approach?\",\"answer\":\"LRA achieves 56% on the 374 analogy questions, which is stated as statistically equivalent to an average human score of 57%, improving over the VSM baseline reported as 47%.\"}]","Similarity of Semantic Relations - Latent Relational Analysis (LRA) - paper | PDF",93]