{"id":463,"date":"2024-02-22T10:50:15","date_gmt":"2024-02-22T09:50:15","guid":{"rendered":"https:\/\/www.ilc.cnr.it\/lucet\/?page_id=463"},"modified":"2024-02-23T15:17:21","modified_gmt":"2024-02-23T14:17:21","slug":"computational-modelling-of-lc-pd","status":"publish","type":"page","link":"https:\/\/www.ilc.cnr.it\/lucet\/work-plan\/computational-modelling-of-lc-pd\/","title":{"rendered":"COMPUTATIONAL MODELLING OF LC\/PD"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Linguistic preprocessing<\/h2>\n\n\n\n<p>Automatic linguistic annotation of texts collected within the project. Existing linguistic annotation tools will be specialized to handle the varieties of language to be dealt with (students\u2019 language, educational materials).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Feature extraction<\/h2>\n\n\n\n<p>Texts enriched with multi-level linguistic annotation will be further inspected to extract and weigh the features of interest. We will start from a battery of tools developed by project partners and already tested in different projects and application scenarios, namely:<\/p>\n\n\n\n<ul>\n<li>the Morpho Complexity Tool<\/li>\n\n\n\n<li>TSOM (Temporal Self-Organizing Map) [TBC]<\/li>\n\n\n\n<li>Profiling-UD<\/li>\n<\/ul>\n\n\n\n<p>These tools will be specialized to meet the project requirements in terms of both the set of features to be tracked and the metrics used to weigh them.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Modelling LC\/PD<\/h2>\n\n\n\n<p>The features identified during the preliminary data analysis, their interactions and their relationships with Processing Difficulty (PD) for different user profiles, will be further investigated by specializing <a href=\"http:\/\/www.italianlp.it\/demo\/read-it\/\" target=\"_blank\" rel=\"noreferrer noopener\">READ-IT<\/a>.<\/p>\n\n\n\n<p>Based on a wide range of linguistic features automatically extracted from texts, <a href=\"http:\/\/www.italianlp.it\/demo\/read-it\/\" target=\"_blank\" rel=\"noreferrer noopener\">READ-IT<\/a>, following a machine learning approach, assigns a weighted value to each feature to produce an overall estimation of the difficulty of a text, as well as a fine-grained estimation of the level of difficulty within each considered linguistic sub-domain.<\/p>\n\n\n\n<p>To unravel the connection between Processing Difficulty (PD) and Linguistic Complexity (LC), multi-task learning approaches will be tested to explore the way the online cognitive metrics collected from the investigated target populations, such as gazing behaviour, can help improve models for readability assessment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Linguistic preprocessing Automatic linguistic annotation of texts collected within the project. Existing linguistic annotation tools will be specialized to handle&hellip;<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":128,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>COMPUTATIONAL MODELLING OF LC\/PD - LuCET Project<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ilc.cnr.it\/lucet\/work-plan\/computational-modelling-of-lc-pd\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"COMPUTATIONAL MODELLING OF LC\/PD - LuCET Project\" \/>\n<meta property=\"og:description\" content=\"Linguistic preprocessing Automatic linguistic annotation of texts collected within the project. 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