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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 Page: 1 2 3 4 5 Hits 1 – 20 of 82 1 An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hirst, Graeme; Rudzicz, Frank. - : Underline Science Inc., 2021 BASE Show details 2 Knowledge Graphs meet Moral Values ... The 28th International Conference on Computational Linguistics 2020; Hirst, Graeme; Hulpuș, Ioana. - : Underline Science Inc., 2020 BASE Show details 3 Text-based inference of moral sentiment change ... Xie, Jing Yi; Pinto, Renato Ferreira; Hirst, Graeme. - : arXiv, 2020 BASE Show details 4 Computational Analysis of Arguments and Persuasive Strategies in Political Discourse Naderi, Nona. - 2020 BASE Show details 5 Knowledge graphs meet moral values Hulpus, Ioana; Kobbe, Jonathan; Stuckenschmidt, Heiner. - : Association for Computational Linguistics, 2020 BASE Show details 6 Linguistic fundamentals for natural language processing II: 100 essentials from semantics and pragmatics Bender, Emily M; Lascarides, Alex; Hirst, Graeme. - : Morgan & Claypool Publishers, 2019 BASE Show details 7 Cross-Lingual Sentiment Analysis Without (Good) Translation ... Abdalla, Mohamed; Hirst, Graeme. - : arXiv, 2017 BASE Show details 8 Exploiting Linguistic Knowledge in Lexical and Compositional Semantic Models Wang, Tong. - 2017 BASE Show details 9 Automatic Text and Speech Processing for the Detection of Dementia Fraser, Kathleen. - 2017 BASE Show details 10 Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments (Dagstuhl Seminar 16161) Cabrio, Elena; Hirst, Graeme; Villata, Serena. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2016. : Dagstuhl Reports. Dagstuhl Reports, Volume 6, Issue 4, 2016 BASE Show details 11 Resolving Shell Nouns Kolhatkar, Varada. - 2015 BASE Show details 12 RST-style Discourse Parsing and Its Applications in Discourse Analysis Feng, Vanessa Wei. - 2015 BASE Show details 13 Patterns of local discourse coherence as a feature for authorship attribution Feng, Vanessa Wei; Hirst, Graeme In: LLC. - Oxford : Oxford Univ. Press 29 (2014) 2, 191 OLC Linguistik Show details 14 Computational Approaches to Style and the Lexicon Brooke, Julian. - 2014 BASE Show details 15 Automated classification of primary progressive aphasia subtypes from narrative speech transcripts ; Automated classification of primary progressive aphasia subtypes from narrative speech samples Rochon, Elizabeth; Fraser, Kathleen C; Graham, Naida L. - : Elsevier, 2014 BASE Show details 16 Computational linguistics Hirst, Graeme In: The Oxford handbook of the history of linguistics (Oxford, 2013), p. 707-726 MPI für Psycholinguistik Show details 17 Computing Lexical Contrast ... Mohammad, Saif M.; Dorr, Bonnie J.; Hirst, Graeme. - : arXiv, 2013 BASE Show details 18 Automated methods for text correction Rozovskaya, Alla. - 2013 Abstract: Development of automatic text correction systems has a long history in natural language processing research. This thesis considers the problem of correcting writing mistakes made by non-native English speakers. We address several types of errors commonly exhibited by non-native English writers – misuse of articles, prepositions, noun number, and verb properties – and build a robust, state-of-the-art system that combines machine learning methods and linguistic knowledge. The proposed approach is distinguished from other related work in several respects. First, several machine learning methods are compared to determine which methods are most effective for this problem. Earlier evaluations, because they are based on incomparable data sets, have questionable conclusions. Our results reverse these conclusions and pave the way for the next contribution. Using the important observation that mistakes made by non-native writers are systematic, we develop models that utilize knowledge about error regularities with minimal annotation costs. Our approach differs from earlier ones that either built models that had no knowledge about error regularities or required a lot of annotated data. Next, we develop special strategies for correcting errors on open-class words. These errors, while being very prevalent among non-native English speakers, are the least studied and are not well-understood linguistically. The challenges that these mistakes present are addressed in a linguistically-informed approach. Finally, a novel global approach to error correction is proposed that considers grammatical dependencies among error types and addresses these via joint learning and joint inference. The systems and techniques described in this thesis are evaluated empirically and competitively in the context of several shared tasks, where they have demonstrated superior performance. In particular, our system ranked first in the most prestigious competition in the natural language processing field, the CoNLL-2013 shared task on text correction. Based on the analysis of this system, four design principles that are crucial for building a state-of-the-art error correction system are identified. Keyword: automated methods for text correction; English as a second language (ESL) error correction; grammatical error correction; text correction URL: http://hdl.handle.net/2142/46875 BASE Hide details 19 Changes in Style in Authors with Alzheimer's Disease Hirst, Graeme; Wei Feng, Vanessa In: English studies. - Abingdon : Routledge, Taylor & Francis Group 93 (2012) 3, 357-370 OLC Linguistik Show details 20 TORGO Database of Dysarthric Articulation Rudzicz, Frank; Hirst, Graeme; van Lieshout, Pascal. - : Linguistic Data Consortium, 2012. : https://www.ldc.upenn.edu, 2012 BASE Show details Page: 1 2 3 4 5

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