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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 – 15 of 15 1 On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries ... Shi, Tianze; Zhao, Chen; Boyd-Graber, Jordan. - : arXiv, 2020 BASE Show details 2 Global Voices: Crossing Borders in Automatic News Summarization ... Nguyen, Khanh; Daumé, Hal. - : arXiv, 2019 BASE Show details 3 Toward Gender-Inclusive Coreference Resolution ... Cao, Yang Trista; Daumé, Hal. - : arXiv, 2019 BASE Show details 4 Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task ... Ettinger, Allyson; Rao, Sudha; Daumé, Hal. - : arXiv, 2017 BASE Show details 5 Discourse-Level Language Understanding with Deep Learning Iyyer, Mohit Nagaraja. - 2017 Abstract: Designing computational models that can understand language at a human level is a foundational goal in the field of natural language processing (NLP). Given a sentence, machines are capable of translating it into many different languages, generating a corresponding syntactic parse tree, marking words that refer to people or places, and much more. These tasks are solved by statistical machine learning algorithms, which leverage patterns in large datasets to build predictive models. Many recent advances in NLP are due to deep learning models (parameterized as neural networks), which bypass user-specified features in favor of building representations of language directly from the text. Despite many deep learning-fueled advances at the word and sentence level, however, computers still struggle to understand high-level discourse structure in language, or the way in which authors combine and order different units of text (e.g., sentences, paragraphs, chapters) to express a coherent message or narrative. Part of the reason is data-related, as there are no existing datasets for many contextual language-based problems, and some tasks are too complex to be framed as supervised learning problems; for the latter type, we must either resort to unsupervised learning or devise training objectives that simulate the supervised setting. Another reason is architectural: neural networks designed for sentence-level tasks require additional functionality, interpretability, and efficiency to operate at the discourse level. In this thesis, I design deep learning architectures for three NLP tasks that require integrating information across high-level linguistic context: question answering, fictional relationship understanding, and comic book narrative modeling. While these tasks are very different from each other on the surface, I show that similar neural network modules can be used in each case to form contextual representations. Keyword: artificial intelligence; Computer science; creative language; deep learning; machine learning; natural language processing; question answering URL: https://doi.org/10.13016/M2930NW6W http://hdl.handle.net/1903/20159 BASE Hide details 6 Predicting the impact of scientific concepts using full‐text features McKeown, Kathy; Daume, Hal; Chaturvedi, Snigdha. - : Association for Computational Linguistics, 2016. : Wiley Periodicals, Inc., 2016 BASE Show details 7 Modeling Dynamic Relationships Between Characters in Literary Novels ... Chaturvedi, Snigdha; Srivastava, Shashank; Daume, Hal. - : arXiv, 2015 BASE Show details 8 Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding Tamchyna, Aleš; Braune, Fabienne; Fraser, Alexander... In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2014) 101, 29-42 OLC Linguistik Show details 9 Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation Ferraro, Jeffrey P; Daumé, Hal; DuVall, Scott L. - : BMJ Publishing Group Ltd, 2013 BASE Show details 10 Computational methods are invaluable for typology, but the models must match the questions : [the universals debate, apropos of Dunn, Greenhill, Levinson & Gray 2011] Daumé, Hal; Levy, Roger In: Linguistic typology. - Berlin [u.a.] : Mouton de Gruyter 15 (2011) 2, 393-399 BLLDB OLC Linguistik Show details 11 Non-Parametric Bayesian Areal Linguistics ... Daumé, Hal. - : arXiv, 2009 BASE Show details 12 Induction of Word and Phrase Alignments for Automatic Document Summarization ... Daumé, Hal; Marcu, Daniel. - : arXiv, 2009 BASE Show details 13 A Bayesian Model for Discovering Typological Implications ... Daumé, Hal; Campbell, Lyle. - : arXiv, 2009 BASE Show details 14 Induction of word and phrase alignments for automatic document summarization Daumé, Hal; Marcu, Daniel In: Computational linguistics. - Cambridge, Mass. : MIT Press 31 (2005) 4, 505-530 BLLDB OLC Linguistik Show details 15 Book Review, Inderjeet Mani: Automatic Summarization, John Benjamins Publishing Co., Amsterdam, The Netherlands, 2001, xi + 286pp. Daumé, Hal In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 18 (2003) 4, 343-348 OLC Linguistik Show details

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