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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...8 Hits 1 – 20 of 157 1 From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ... Mather, Brodie; Dorr, Bonnie J; Dalton, Adam. - : arXiv, 2022 BASE Show details 2 Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge ... Dorr, Bonnie J.; Bhatia, Archna; Dalton, Adam. - : arXiv, 2020 BASE Show details 3 Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation ... Bhatia, Archna; Dalton, Adam; Mather, Brodie. - : arXiv, 2020 BASE Show details 4 Statistical modality tagging from rule-based annotations and crowdsourcing ... Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : arXiv, 2015 BASE Show details 5 Use of Modality and Negation in Semantically-Informed Syntactic MT ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2015 BASE Show details 6 A Modality Lexicon and its use in Automatic Tagging ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2014 BASE Show details 7 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : arXiv, 2014 BASE Show details 8 Computing Lexical Contrast ... Mohammad, Saif M.; Dorr, Bonnie J.; Hirst, Graeme. - : arXiv, 2013 BASE Show details 9 Use of Modality and Negation in Semantically-Informed Syntactic MT Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J... In: DTIC (2012) BASE Show details 10 Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012 BASE Show details 11 Use of Modality and Negation in Semantically-Informed Syntactic MT Bloodgood, Michael; Filardo, Nathaniel; Levin, Lori. - : MIT Press, 2012 BASE Show details 12 Citation Handling: Processing Citation Texts in Scientific Documents Whidby, Michael Alan. - 2012 BASE Show details 13 Measuring Degrees of Semantic Opposition ... Mohammad, Saif M.; Dorr, Bonnie J.; Hirst, Graeme. - : National Research Council Canada, 2011 BASE Show details 14 Citation Handling for Improved Summarization of Scientific Documents Whidby, Michael; Zajic, David; Dorr, Bonnie. - 2011 BASE Show details 15 Interlingual annotation of parallel text corpora: a new framework for annotation and evaluation Siddharthan, Advaith; Habash, Nizar; Passonneau, Rebecca J.... In: Natural language engineering. - Cambridge : Cambridge University Press 16 (2010) 3, 197-243 BLLDB OLC Linguistik Show details 16 TER-Plus: paraphrase, semantic, and alignment enhancements to translation edit rate Schwartz, Richard; Snover, Matthew G.; Dorr, Bonnie Jean... In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 23 (2010) 2-3, 117-127 BLLDB OLC Linguistik Show details 17 Symbolic-to-statistical hybridization: extending generation-heavy machine translation Dorr, Bonnie Jean; Monz, Christof; Habash, Nizar In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 23 (2010) 1, 23-63 BLLDB OLC Linguistik Show details 18 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : Digital Repository at the University of Maryland, 2010 BASE Show details 19 The Circle of Meaning: From Translation to Paraphrasing and Back Madnani, Nitin. - 2010 Abstract: The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection. Keyword: Artificial Intelligence; Computational Linguistics; Computer Science; Language; Linguistics; Machine Translation; Natural Language Processing; Paraphrase Generation URL: http://hdl.handle.net/1903/10502 BASE Hide details 20 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach Baker, Kathryn; Piatko, Christine; Miller, Scott. - 2010 BASE Show details Page: 1 2 3 4 5...8

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