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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 – 14 of 14 1 Statistische Modelle für die hierarchische phrasenbasierte maschinelle Übersetzung ... : Statistical models for hierarchical phrase-based machine translation ... Huck, Matthias. - : RWTH Aachen University, 2019 BASE Show details 2 Logic of Shared Significations on Internet Relay Chat ... Mercier, David-Olivier. - : Université d'Ottawa / University of Ottawa, 2019 BASE Show details 3 Methods for taking semantic graphs apart and putting them back together again ... Groschwitz, Jonas. - : Universität des Saarlandes, 2019 BASE Show details 4 Proposition-based summarization with a coherence-driven incremental model ... Fang, Yimai. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 5 Learning to Order & Learning to Correct Schmaltz, Allen. - 2019 Abstract: We investigate three core tasks in Natural Language Processing that are informative toward building tools for writing assistance---word ordering, grammatical error identification, and grammar correction---shedding new light on old questions and providing new modeling approaches for real-world applications. The first task, word ordering, aims to correctly order a randomly shuffled sentence. Via this diagnostic task, we find evidence that strong surface-level models are at least as effective as the models utilizing explicit syntactic structures for modeling ordering constraints, and we incorporate this insight when approaching the end-user grammar tasks. An advantage of surface-level models is that additional training data is relatively straightforward to acquire. We perform an analysis of word ordering output with a surface-level model at scale. We find that remaining errors are associated with greater proportions of n-grams unseen in training, highlighting both a path for future improvements in effectiveness and the clear brittleness of such models, with implications for generation models, more generally. The second task, grammatical error identification, seeks to classify whether or not a sentence contains a grammatical error. The third task, grammar correction, seeks to transduce a sentence that may or may not have errors into a corrected version. For both of these tasks, we utilize insights from the diagnostic word ordering task and adapt modern sequence models to improve effectiveness over contemporary work. Modern sequence models for the grammar tasks require significant amounts of data to be effective. In a final section, we propose methods for noising well-formed text from limited amounts of human annotated data. With our proposed data augmentation scheme, we demonstrate that sequence models can be trained with synthetic data to approach the levels of effectiveness of models trained on substantially more human annotated sentences. At the same time, such semi-supervised approaches are still clearly weaker than models trained with very large amounts of annotated data. ; Engineering and Applied Sciences - Computer Science Keyword: computational linguistics; grammar correction; natural language processing; word ordering URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029644 BASE Hide details 6 Automatic syntactic analysis of learner English ... Huang, Yan. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 7 Parsimonious Vole : a Systemic Functional Parser for English ... Costetchi, Eugeniu. - : Universität Bremen, 2019 BASE Show details 8 Linguistic Phylogeny with Bayesian Markov Chain Monte Carlo: The Case of Indo-European Tyndall, Stephen. - 2019 BASE Show details 9 Machine learning methods for vector-based compositional semantics Maillard, Jean. - : University of Cambridge, 2019. : Department of Computer Science and Technology, 2019. : St John's College, 2019 BASE Show details 10 Learning meaning representations for text generation with deep generative models Cao, Kris. - : University of Cambridge, 2019. : Department of Computer Science and Technology, 2019. : Clare, 2019 BASE Show details 11 A study of model parameters for scaling up word to sentence similarity tasks in distributional semantics Chetehouna, Mouna. - : Queen Mary University of London, 2019 BASE Show details 12 Methods for taking semantic graphs apart and putting them back together again Groschwitz, Jonas. - : Saarländische Universitäts- und Landesbibliothek, 2019 BASE Show details 13 Issues in Named Entity Recognition on Early Modern English Letters Woldenga-Racine, Vanessa. - 2019 BASE Show details 14 Logic of Shared Significations on Internet Relay Chat Mercier, David-Olivier. - : Université d'Ottawa / University of Ottawa, 2019 BASE Show details

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