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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 6 Hits 1 – 20 of 107 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 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 16 The Circle of Meaning: From Translation to Paraphrasing and Back Madnani, Nitin. - 2010 BASE Show details 17 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach Baker, Kathryn; Piatko, Christine; Miller, Scott. - 2010 BASE Show details 18 A Modality Lexicon and its use in Automatic Tagging Dorr, Bonnie; Bloodgood, Michael; Filardo, Nathaniel. - : European Language Resources Association, 2010 BASE Show details 19 Improving Statistical Machine Translation Using Comparable Corpora Snover, Matthew Garvey. - 2010 BASE Show details 20 Language Understanding Annotation Corpus Diab, Mona; Dorr, Bonnie; Levin, Lori; Mitamura, Teruko; Passonneau, Rebecca; Rambow, Owen; Ramshaw, Lance. - : Linguistic Data Consortium, 2009. : https://www.ldc.upenn.edu, 2009 Abstract: *Introduction* The Language Understanding Annotation Corpus emerged from a series of interdisciplinary meetings on semantics and pragmatics hosted by the Human Language Technology Center of Excellence at Johns Hopkins University. The participants were researchers from Raytheon BBN Technologies, Carnegie Mellon University and Columbia University who were developing representations of text semantics, machine translation and summarization systems. The resulting corpus contains over 9000 words of English text (6949 words) and Arabic text (2183 words) annotated for committed belief, event and entity coreference, dialog acts and temporal relations. The source materials were chosen from various genres to represent "informal input," that is, text that contains colloquial forms. The documents in the corpus include excerpts from newswire stories, telephone conversation transcripts, emails, contracts and written instructions. The problem was modeled as an extended exercise in extracting information elements from a "document" (that is, from discrete language records in written or spoken forms). The goal was to answer two broad questions: * What are the elements of knowledge that can be derived from a document? * Can the representation, and hence, the annotation, be laid out in terms of iterative layers, the accumulation of which would represent the sum of the knowledge? The annotations attempted to resolve these questions in the following ways: * Belief/Opinion/Confidence. Committed belief annotation distinguishes between statements which assert belief or opinion, those which contain speculation, and statements which convey facts or otherwise do not convey belief. The goal is to be able to determine automatically from a given text what beliefs can be ascribed to the author and with what strength the author holds those beliefs. * Dialog Acts. Dialog act annotation seeks to determine the forward and backward links between pairs of dialog acts. * Coreference (entities and events). Event coreferences indicate which events are related to other events at the document level. Entity relations within these related events provide further information about e.g., the main actors, targets and causes of the events. * Temporal relations. Temporal annotations mark the temporal relationship between the different events and time anchors mentioned in a document, that is, it highlights what the text is saying about the time line of time-mentions. URL: https://catalog.ldc.upenn.edu/LDC2009T10 BASE Hide details Page: 1 2 3 4 5 6

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