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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 – 17 of 17 1 Classifying Bias in Large Multilingual Corpora via Crowdsourcing and Topic Modeling Caljean, Brianna; Calvert, Katherine; Chang, Ashley. - 2018 BASE Show details 2 Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ... Zajic, David; Maxwell, Michael; Doermann, David. - : arXiv, 2014 BASE Show details 3 A random forest system combination approach for error detection in digital dictionaries ... Bloodgood, Michael; Ye, Peng; Rodrigues, Paul. - : arXiv, 2014 BASE Show details 4 Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ... Rodrigues, Paul; Zajic, David; Doermann, David. - : arXiv, 2014 BASE Show details 5 A random forest system combination approach for error detection in digital dictionaries Rodrigues, Paul; Zajic, David; Doermann, David. - : Association for Computational Linguistics, 2012 BASE Show details 6 Citation Handling: Processing Citation Texts in Scientific Documents Whidby, Michael Alan. - 2012 BASE Show details 7 Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling Rodrigues, Paul; Zajic, David; Doermann, David... In: Electronic lexicography in the 21st Century: New Applications for New Users. Proceedings of eLex2011, Bled, Slowenien, 10 - 12 November 2011 (2011), 227-232 IDS OBELEX meta Show details 8 Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language Zajic, David; Maxwell, Michael; Doermann, David... In: Electronic lexicography in the 21st Century: New Applications for New Users. Proceedings of eLex2011, Bled, Slowenien, 10 - 12 November 2011 (2011), 297-301 IDS OBELEX meta Show details 9 Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ... Zajic, David; Maxwell, Michael; Doermann, David. - : Digital Repository at the University of Maryland, 2011 BASE Show details 10 Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ... Rodrigues, Paul; Zajic, David; Doermann, David. - : Digital Repository at the University of Maryland, 2011 BASE Show details 11 Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language Zajic, David; Maxwell, Michael; Doermann, David. - : Trojina Institute for Applied Slovene Studies, 2011 BASE Show details 12 Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling Bloodgood, Michael; Ye, Peng; Rodrigues, Paul. - : Trojina Institute for Applied Slovene Studies, 2011 BASE Show details 13 Citation Handling for Improved Summarization of Scientific Documents Whidby, Michael; Zajic, David; Dorr, Bonnie. - 2011 BASE Show details 14 Error Correction for Arabic Dictionary Lookup Rytting, C. Anton; Rodrigues, Paul; Buckwalter, Tim... In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), Valetta, 17 - 23 May 2010 (2010), 263-268 IDS OBELEX meta Show details 15 Multiple Alternative Sentene Compressions as a Tool for Automatic Summarization Tasks Zajic, David Michael. - 2007 Abstract: Automatic summarization is the distillation of important information from a source into an abridged form for a particular user or task. Many current systems summarize texts by selecting sentences with important content. The limitation of extraction at the sentence level is that highly relevant sentences may also contain non-relevant and redundant content. This thesis presents a novel framework for text summarization that addresses the limitations of sentence-level extraction. Under this framework text summarization is performed by generating Multiple Alternative Sentence Compressions (MASC) as candidate summary components and using weighted features of the candidates to construct summaries from them. Sentence compression is the rewriting of a sentence in a shorter form. This framework provides an environment in which hypotheses about summarization techniques can be tested. Three approaches to sentence compression were developed under this framework. The first approach, HMM Hedge, uses the Noisy Channel Model to calculate the most likely compressions of a sentence. The second approach, Trimmer, uses syntactic trimming rules that are linguistically motivated by Headlinese, a form of compressed English associated with newspaper headlines. The third approach, Topiary, is a combination of fluent text with topic terms. The MASC framework for automatic text summarization has been applied to the tasks of headline generation and multi-document summarization, and has been used for initial work in summarization of novel genres and applications, including broadcast news, email threads, cross-language, and structured queries. The framework supports combinations of component techniques, fostering collaboration between development teams. Three results will be demonstrated under the MASC framework. The first is that an extractive summarization system can produce better summaries by automatically selecting from a pool of compressed sentence candidates than by automatically selecting from unaltered source sentences. The second result is that sentence selectors can construct better summaries from pools of compressed candidates when they make use of larger candidate feature sets. The third result is that for the task of Headline Generation, a combination of topic terms and compressed sentences performs better then either approach alone. Experimental evidence supports all three results. Keyword: Automatic Summarization; Computer Science; Human Language Technology; Natural Language Processing; Sentence Compression URL: http://hdl.handle.net/1903/6729 BASE Hide details 16 Headline Generation for Written and Broadcast News Zajic, David; Dorr, Bonnie; Schwartz, Richard In: DTIC (2005) BASE Show details 17 Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation Dorr, Bonnie; Zajic, David; Schwartz, Richard In: DTIC (2003) BASE Show details

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