{"id":307,"date":"2019-11-07T11:39:58","date_gmt":"2019-11-07T10:39:58","guid":{"rendered":"https:\/\/site.uit.no\/clear\/?p=307"},"modified":"2019-11-12T21:35:54","modified_gmt":"2019-11-12T20:35:54","slug":"robert-reynolds-phd","status":"publish","type":"post","link":"https:\/\/site.uit.no\/clear\/2019\/11\/07\/robert-reynolds-phd\/","title":{"rendered":"Robert Reynolds: PhD project"},"content":{"rendered":"

Dissertation title:<\/strong> \u201cRussian natural language processing for computer- assisted language learning: Capturing the benefits of deep morphological analysis in real-life applications\u201d<\/p>\n

Supervisors:\u00a0<\/strong>Laura Janda and Detmar Meurers<\/p>\n

Summary:<\/strong><\/p>\n

In this dissertation, I investigate practical and theoretical issues surrounding the use of natural language processing technology in the context of Russian Computer-Assisted Language-Learning, with particular emphasis on morphological analysis. In Part I, I present linguistic and practical issues surrounding the development and evaluation of two foundational technologies: a two-level morphological analyzer, and a constraint grammar to contextually disambiguate homonymy in the analyzer\u2019s output. The analyzer was specially designed for L2 learner applications\u2014with stress annotation and rule-based morphosyntactic disambiguation\u2014and it is competitive with state-of-the-art Russian analyzers. The constraint grammar is designed to have high recall, allowing an L2-learner application to base decisions on all possible readings, and not just the single most likely reading. The constraint grammar resolves 44% of the ambiguity output by the morphological analyzer. A voting setup combining the constraint grammar with a trigram hidden markov model tagger demonstrates how a high-recall grammar can boost performance of probabilistic taggers, which are better suited to capturing highly idiosyncratic facts about collocational tendencies. In Part II, I present linguistic, theoretical, practical issues surrounding the application of the morphological analyzer and constraint grammar to three real-life computer-assisted language-learning tasks: automatic stress annotation, automatic grammar exercise generation from authentic texts, and automatic evaluation of text readability. The automatic stress placement task is vital for Russian language-learning applications. The morphological analyzer and constraint grammar yield state-of-the-art results, resolving 42% of stress ambiguity in a corpus of running text. In order to demonstrate the value of a high-recall constraint grammar, I developed Russian grammar activities for the VIEW platform, a system for providing automatic Visual Input Enhancement of Web documents. This system allows teachers and learners to automatically generate grammatical highlighting, identification activities, multiple-choice activities, and fill-in-the-blank activities, enabling them to study grammar using texts that are interesting or relevant to them. I show that the morphological analysis described above is instrumental not only for generating exercises, but also for providing adaptive feedback, a feature which typically requires encoding specific learner language features. A final test-case for morphological analysis in Russian language-learning is automatic readability assessment, which can help learners and teachers find texts at appropriate reading levels. I show that features based on morphology are among the most informative for L2 readability assessment.<\/p>\n

Defense:<\/strong> August 15, 2016<\/p>\n

Opponents:\u00a0<\/strong>Mathias Schulze and Markus Dickinson<\/p>\n

Available at MUNIN:<\/strong>\u00a0https:\/\/munin.uit.no\/handle\/10037\/9685<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Dissertation title: \u201cRussian natural language processing for computer- assisted language learning: Capturing the benefits of deep morphological analysis in real-life […]<\/p>\n","protected":false},"author":1063,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-307","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/posts\/307","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/users\/1063"}],"replies":[{"embeddable":true,"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/comments?post=307"}],"version-history":[{"count":10,"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/posts\/307\/revisions"}],"predecessor-version":[{"id":741,"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/posts\/307\/revisions\/741"}],"wp:attachment":[{"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/media?parent=307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/categories?post=307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/site.uit.no\/clear\/wp-json\/wp\/v2\/tags?post=307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}