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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 Hits 1 – 20 of 71 1 How Well Do LSTM Language Models Learn Filler-gap Dependencies? Ozaki, Satoru; Yurovsky, Dan; Levin, Lori In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 2 Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation ... Zhou, Zhong; Levin, Lori; Mortensen, David R.. - : arXiv, 2019 BASE Show details 3 A Resource for Computational Experiments on Mapudungun ... Duan, Mingjun; Fasola, Carlos; Rallabandi, Sai Krishna. - : arXiv, 2019 BASE Show details 4 Language Technologies for Humanitarian Aid ... Carbonell, Jaime G.; Lavie, Alon; Levin, Lori. - : Figshare, 2018 BASE Show details 5 Language Technologies for Humanitarian Aid ... Carbonell, Jaime G.; Lavie, Alon; Levin, Lori. - : Figshare, 2018 BASE Show details 6 Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ... Chaudhary, Aditi; Zhou, Chunting; Levin, Lori. - : arXiv, 2018 BASE Show details 7 ParaMor: Finding Paradigms across Morphology ... Monson, Christian; Carbonell, Jaime G.; Lavie, Alon. - : Figshare, 2018 BASE Show details 8 ParaMor: Minimally-Supervised Induction of Paradigm Structure and Morphological Analysis ... Monson, Christian; Carbonell, Jaime G.; Lavie, Alon. - : Figshare, 2018 BASE Show details 9 ParaMor: Finding Paradigms across Morphology ... Monson, Christian; Carbonell, Jaime G.; Lavie, Alon. - : Figshare, 2018 BASE Show details 10 POS error detection in automatically annotated corpora Rehbein, Ines [Verfasser]; Levin, Lori [Herausgeber]; Stede, Manfred [Herausgeber]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2016 DNB Subject Category Language Show details 11 Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ... Tsvetkov, Yulia; Sitaram, Sunayana; Faruqui, Manaal. - : arXiv, 2016 BASE Show details 12 Proceedings of the Grammar Engineering Across Frameworks (GEAF) 2015 Workshop Bender, Emily,; Levin, Lori; Müller, Stefan. - : HAL CCSD, 2015. : Association for Computational Linguistics, 2015 In: The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01181344 ; The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Jul 2015, Beijing, China. Association for Computational Linguistics, 2015 (2015) BASE Show details 13 Statistical modality tagging from rule-based annotations and crowdsourcing ... Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : arXiv, 2015 BASE Show details 14 Use of Modality and Negation in Semantically-Informed Syntactic MT ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2015 BASE Show details 15 STTS 2.0? Improving the tagset for the part-of-speech-tagging of German spoken data Westpfahl, Swantje [Verfasser]; Levin, Lori [Herausgeber]; Stede, Manfred [Herausgeber]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2014 DNB Subject Category Language Show details 16 A Modality Lexicon and its use in Automatic Tagging ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2014 BASE Show details 17 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : arXiv, 2014 BASE Show details 18 Use of Modality and Negation in Semantically-Informed Syntactic MT Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J... In: DTIC (2012) BASE Show details 19 Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012 BASE Show details 20 Use of Modality and Negation in Semantically-Informed Syntactic MT Bloodgood, Michael; Filardo, Nathaniel; Levin, Lori; Dorr, Bonnie; Baker, Kathryn; Piatko, Christine; Miller, Scott; Callison-Burch, Chris. - : MIT Press, 2012 Abstract: This article describes the resource- and system-building efforts of an 8-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation), and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set. We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. Although the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described here. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu–English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality. ; This work was supported, in part, by the Johns Hopkins Human Language Technology Center of Excellence (HLTCOE), by the National Science Foundation under grant IIS-0713448, and by BBN Technologies under GALE DARPA/IPTO contract no. HR0011-06-C-0022. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. Keyword: artificial intelligence; computational linguistics; computer science; human language technology; machine translation; modality; natural language processing; negation; semantically-informed machine translation; statistical machine translation; statistical methods; translation technology URL: http://hdl.handle.net/1903/15547 BASE Hide details Page: 1 2 3 4

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