Newcastle University eTheses: The design and semantic analysis of a programming language and its compiler{"id":5757,"date":"2023-06-02T09:32:01","date_gmt":"2023-06-02T09:32:01","guid":{"rendered":"https:\/\/roulottemagazine.com\/?p=5757"},"modified":"2023-09-23T11:31:54","modified_gmt":"2023-09-23T11:31:54","slug":"newcastle-university-etheses-the-design-and","status":"publish","type":"post","link":"https:\/\/roulottemagazine.com\/CA\/2023\/06\/newcastle-university-etheses-the-design-and\/","title":{"rendered":"Newcastle University eTheses: The design and semantic analysis of a programming language and its compiler"},"content":{"rendered":"<p><h1>Using Explicit Semantic Analysis for Cross-Lingual Link Discovery Open Research Online<\/h1>\n<\/p>\n<p><img class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' src=\"data:image\/jpeg;base64,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\" width=\"302px\" alt=\"semantic analytics\"\/><\/p>\n<p><p>The overall focus of the research project was to address \u201cthe analysis and visualization of multiple sources of multi-modal data that may be partial, unreliable and contradictory\u201d. Making Sense was awarded total funding of \u00a32.1 million from the EPSRC&nbsp;Global Uncertainties fund. The project involved 9 institutions from across the UK and  was led by Imperial College London. This is a adopts a proprietary syntax rather than a more widely adopted schema definition language. One limitation of database templates is that the names of tables and columns CANNOT contain spaces. This is likely due to the limitations of the underlying parquet format used to store the data.<\/p>\n<\/p>\n<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' src=\"data:image\/jpeg;base64,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\" width=\"304px\" alt=\"semantic analytics\"\/><\/p>\n<p><p>These challenges include ambiguity and polysemy, idiomatic expressions, domain-specific knowledge, cultural and linguistic diversity, and computational complexity. The development of curriculum and access to educational resources related to applied computing is lacking for students in K-12 schools particularly in rural areas, despite the large and growing <a href=\"https:\/\/www.metadialog.com\/\">https:\/\/www.metadialog.com\/<\/a> demand for computing skills in the job market. Open-text feedback was collected before, during, and immediately after the workshop in response to multiple types of formative assessments. In this paper, we present several forms of data representation from exploratory textual analyses based on the feedback collected from the workshop participants.<\/p>\n<\/p>\n<p><h2>1 About Explicit Semantic Analysis<\/h2>\n<\/p>\n<p><p>The strength of the association is captured by the weight value of each attribute-concept pair. The attribute-concept matrix is stored as a reverse index that lists the most important concepts for each attribute. In Oracle database 12c Release 2, Explicit Semantic Analysis (ESA) was introduced as an unsupervised algorithm used by Oracle Data Mining for Feature Extraction. <a href=\"https:\/\/www.metadialog.com\/blog\/semantic-analysis-in-nlp\/\">semantic analytics<\/a> Starting  from Oracle Database 18c, ESA is enhanced as a supervised algorithm for Classification. A visual representation showing the USAS tagset heirarchy is<\/p>\n<p>now on-line, along with those for the Louw-Nida model<\/p>\n<p>and the Hallig\/Von Wartburg\/Schmidt\/Wilson Model. The full tagset is available on-line in <\/p>\n<p>plain text form and <\/p>\n<p>formatted on one page in PDF.<\/p>\n<\/p>\n<div style='border: grey dashed 1px;padding: 11px;'>\n<h3>Google unveils generative AI integrations for data tools &#8211; TechTarget<\/h3>\n<p>Google unveils generative AI integrations for data tools.<\/p>\n<p>Posted: Mon, 28 Aug 2023 07:00:00 GMT [<a href='https:\/\/news.google.com\/rss\/articles\/CBMiemh0dHBzOi8vd3d3LnRlY2h0YXJnZXQuY29tL3NlYXJjaGJ1c2luZXNzYW5hbHl0aWNzL25ld3MvMzY2NTUwMDE5L0dvb2dsZS11bnZlaWxzLWdlbmVyYXRpdmUtQUktaW50ZWdyYXRpb25zLWZvci1kYXRhLXRvb2xz0gEA?oc=5' rel=\"nofollow\">source<\/a>]<\/p>\n<\/div>\n<p><p>It supports decision-making and risk&nbsp;management, and helps deal with an ever-increasing volume of information. When there are missing values in columns with simple data types (not nested), ESA replaces missing categorical values with the mode and missing numerical values with the mean. The algorithm replaces sparse numeric data with zeros and sparse categorical data with zero vectors.<\/p>\n<\/p>\n<p><h2>Extensible semantic Structured Data analysis library for Microdata and JSON-LD<\/h2>\n<\/p>\n<p><p>It is a very manual process, where the dictionaries are built up over time by a data engineer. For the knife crime process, it took months of manual reading thousands of records with my colleague to build up the dictionaries, and constantly refining. Also, it leverages a lot of local subject matter expertise, which while useful clearly puts additional strain on already over-stretched resources.<\/p>\n<\/p>\n<p><p>For example, in England and Wales, police forces report their crime figures on a monthly\/ quarterly\/ bi-annual\/ annual basis. Fulfilling the reporting requirement means an analyst must manually search through 8 different fields looking for the world \u2018knife\u2019, working out at roughly 36 days work a year. However, one of the challenges is that there can be a lot of misreported figures in terms of the total number of a particular crime. Through these techniques, the personal assistant can interpret  and respond to user inputs with higher accuracy, exhibiting the practical impact of semantic analysis in a real-world setting. \u00cf\u00bb\u00bf Abstract Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation.<\/p>\n<\/p>\n<div itemScope itemProp=\"mainEntity\" itemType=\"https:\/\/schema.org\/Question\">\n<div itemProp=\"name\">\n<h2>What are the real life applications of semantic processing?<\/h2>\n<\/div>\n<div itemScope itemProp=\"acceptedAnswer\" itemType=\"https:\/\/schema.org\/Answer\">\n<div itemProp=\"text\">\n<ul>\n<li>Supply Chain Management &#x2013; Biogen Idec.<\/li>\n<li>Media Management &#x2013; BBC.<\/li>\n<li>Data Integration in Oil &amp; Gas &#x2013; Chevron.<\/li>\n<li>Web Search and Ecommerce.<\/li>\n<\/ul>\n<\/div><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[68],"tags":[],"_links":{"self":[{"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/posts\/5757"}],"collection":[{"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/comments?post=5757"}],"version-history":[{"count":1,"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/posts\/5757\/revisions"}],"predecessor-version":[{"id":5758,"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/posts\/5757\/revisions\/5758"}],"wp:attachment":[{"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/media?parent=5757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/categories?post=5757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/roulottemagazine.com\/wp-json\/wp\/v2\/tags?post=5757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}