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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 – 10 of 10 1 English machine reading comprehension: new approaches to answering multiple-choice questions Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021 In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021) Abstract: Reading comprehension is often tested by measuring a person or system’s ability to answer questions about a given text. Machine reading comprehension datasets have proliferated in recent years, particularly for the English language. The aim of this thesis is to investigate and improve data-driven approaches to automatic reading comprehension. Firstly, I provide a full classification of question and answer types for the reading comprehension task. I also present a systematic overview of English reading comprehension datasets (over 50 datasets). I observe that the majority of questions were created using crowdsourcing and the most popular data source is Wikipedia. There is also a lack of why, when, and where questions. Additionally, I address the question “What makes a dataset difficult?” and highlight the difference between datasets created for people and datasets created for machine reading comprehension. Secondly, focusing on multiple-choice question answering, I propose a computationally light method for answer selection based on string similarities and logistic regression. At the time (December 2017), the proposed approach showed the best performance on two datasets (MovieQA and MCQA: IJCNLP 2017 Shared Task 5 Multi-choice Question Answering in Examinations) outperforming some CNN-based methods. Thirdly, I investigate methods for Boolean Reading Comprehension tasks including the use of Knowledge Graph (KG) information for answering questions. I provide an error analysis of a transformer model’s performance on the BoolQ dataset. This reveals several important issues such as unstable model behaviour and some issues with the dataset itself. Experiments with incorporating knowledge graph information into a baseline transformer model do not show a clear improvement due to a combination of the model’s ability to capture new information, inaccuracies in the knowledge graph, and imprecision in entity linking. Finally, I develop a Boolean Reading Comprehension dataset based on spontaneously user-generated questions and reviews which is extremely close to a real-life question-answering scenario. I provide a classification of question difficulty and establish a transformer-based baseline for the new proposed dataset. Keyword: Artificial intelligence; Computational linguistics; Information retrieval; Machine learning; machine reading comprehension; question answering; transformer language models URL: http://doras.dcu.ie/26534/ BASE Hide details 2 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 3 Promoting user engagement and learning in search tasks by effective document representation Arora, Piyush. - : Dublin City University. School of Computing, 2018. : Dublin City University. ADAPT, 2018 In: Arora, Piyush orcid:0000-0002-4261-2860 (2018) Promoting user engagement and learning in search tasks by effective document representation. PhD thesis, Dublin City University. (2018) BASE Show details 4 Learning to represent, categorise and rank in community question answering Bogdanova, Daria. - : Dublin City University. School of Computing, 2018 In: Bogdanova, Daria (2018) Learning to represent, categorise and rank in community question answering. PhD thesis, Dublin City University. (2018) BASE Show details 5 Report on ACL survey on preprint publishing and reviewing Foster, Jennifer; Nivre, Joakim; Zhao, Shiqi. - : Association for Computational Linguistics (ACL), 2017 In: Foster, Jennifer orcid:0000-0002-7789-4853 , Hearst, Marti, Nivre, Joakim orcid:0000-0002-7873-3971 and Zhao, Shiqi (2017) Report on ACL survey on preprint publishing and reviewing. Policy Report. Association for Computational Linguistics (ACL). (2017) BASE Show details 6 Irish dependency treebanking and parsing Lynn, Teresa. - : Dublin City University. School of Computing, 2016. : Dublin City University. ADAPT, 2016 In: Lynn, Teresa (2016) Irish dependency treebanking and parsing. PhD thesis, Dublin City University. (2016) BASE Show details 7 The role of syntax and semantics in machine translation and quality estimation of machine-translated user-generated content Zadeh Kaljahi, Rasoul Samad. - : Dublin City University. School of Computing, 2015. : Dublin City University. National Centre for Language Technology (NCLT), 2015. : Dublin City University. ADAPT, 2015 In: Zadeh Kaljahi, Rasoul Samad (2015) The role of syntax and semantics in machine translation and quality estimation of machine-translated user-generated content. PhD thesis, Dublin City University. (2015) BASE Show details 8 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 9 A case study of the process of formulating a strategic plan for the delivery of mental health services in an urban school district Foster, Jennifer. - 2010 BASE Show details 10 Good reasons for noting bad grammar : empirical investigations into the parsing of ungrammatical written English Foster, Jennifer. - : Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2005 BASE Show details

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