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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Automatic processing of code-mixed social media content Barman, Utsab. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019 In: Barman, Utsab (2019) Automatic processing of code-mixed social media content. PhD thesis, Dublin City University. (2019) BASE Show details 2 Code mixing: a challenge for language identification in the language of social media Barman, Utsab; Das, Amitava; Wagner, Joachim... In: Barman, Utsab, Das, Amitava orcid:0000-0003-3418-463X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2014) Code mixing: a challenge for language identification in the language of social media. In: First Workshop on Computational Approaches to Code Switching, 25 Oct 2014, Doha, Qatar. (2014) BASE Show details 3 Detecting grammatical errors with treebank-induced, probabilistic parsers Wagner, Joachim. - : Dublin City University. School of Computing, 2012 In: Wagner, Joachim orcid:0000-0002-8290-3849 (2012) Detecting grammatical errors with treebank-induced, probabilistic parsers. PhD thesis, Dublin City University. (2012) BASE Show details 4 Judging grammaticality: experiments in sentence classification Wagner, Joachim; Foster, Jennifer; van Genabith, Josef In: Wagner, Joachim orcid:0000-0002-8290-3849 , Foster, Jennifer orcid:0000-0002-7789-4853 and van Genabith, Josef orcid:0000-0003-1322-7944 (2009) Judging grammaticality: experiments in sentence classification. CALICO Journal, 26 (3). pp. 474-490. ISSN 0742-7778 (2009) Abstract: A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners. Keyword: Computational linguistics; Language; Machine learning URL: http://doras.dcu.ie/15662/ BASE Hide details

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