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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 Hits 1 – 20 of 22 1 Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models ... Guo, Meiqi; Hwa, Rebecca; Lin, Yu-Ru. - : arXiv, 2020 BASE Show details 2 Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models ... The 28th International Conference on Computational Linguistics 2020; Chung, Wen-Ting; Guo, Meiqi. - : Underline Science Inc., 2020 BASE Show details 3 MimicProp: Learning to Incorporate Lexicon Knowledge into Distributed Word Representation for Social Media Analysis Guo, Meiqi; Mert Ertugrul, Ali; Lin, Yu-Ru... In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media; 738-749 ; 2334-0770 ; 2162-3449 (2020) BASE Show details 4 Report on EMNLP Reviewer Survey Névéol, Aurélie; Fort, Karën; Hwa, Rebecca In: https://hal.archives-ouvertes.fr/hal-01660886 ; [Technical Report] Association for computational linguistics. 2017 (2017) BASE Show details 5 Word Re-Embedding via Manifold Dimensionality Retention Hasan, Souleiman; Curry, Edward. - : Association for Computational Linguistics (ACL), 2017 BASE Show details 6 Regression for machine translation evaluation at the sentence level Albrecht, Joshua S.; Hwa, Rebecca In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 22 (2008) 1-2, 1-27 BLLDB OLC Linguistik Show details 7 Heuristic Sample Selection to Minimize Reference Standard Training Set for a Part-Of-Speech Tagger Liu, Kaihong; Chapman, Wendy; Hwa, Rebecca. - : American Medical Informatics Association, 2007 BASE Show details 8 Parsing Arabic Dialects Habash, Nizar Y.; Rambow, Owen C.; Chiang, David. - : Technical report, JHU SummerWorkshop, 2006 BASE Show details 9 Parsing Arabic Dialects ... Habash, Nizar Y.; Rambow, Owen C.; Chiang, David. - : Columbia University, 2006 BASE Show details 10 Bootstrapping parsers via syntactic projection across parallel texts Hwa, Rebecca; Resnik, Philip; Weinberg, Amy... In: Natural language engineering. - Cambridge : Cambridge University Press 11 (2005) 3, 311-325 BLLDB Show details 11 Breaking the Resource Bottleneck for Multilingual Parsing Hwa, Rebecca; Resnik, Philip; Weinberg, Amy In: DTIC AND NTIS (2005) BASE Show details 12 Sample Selection for Statistical Parsing Hwa, Rebecca In: Computational linguistics. - Cambridge, Mass. : MIT Press 30 (2004) 3, 253-276 OLC Linguistik Show details 13 Sample selection for statistical parsing Hwa, Rebecca In: Computational linguistics. - Cambridge, Mass. : MIT Press 30 (2004) 3, 253-276 BLLDB Show details 14 Evaluating Translational Correspondence Using Annotation Projection Hwa, Rebecca; Resnik, Philip; Weinberg, Amy... In: DTIC (2003) BASE Show details 15 Evaluating Translational Correspondence using Annotation Projection Kolak, Okan; Resnik, Philip; Hwa, Rebecca. - 2003 BASE Show details 16 Improved Word-Level Alignment: Injecting Knowledge about MT Divergences Dorr, Bonnie J.; Pearl, Lisa; Hwa, Rebecca... In: DTIC (2002) BASE Show details 17 Word-level Alignment for Multilingual Resource Acquisition Lopez, Adam; Nossal, Michael; Hwa, Rebecca... In: DTIC (2002) BASE Show details 18 Breaking the Resource Bottleneck for Multilingual Parsing Hwa, Rebecca; Weinberg, Amy; Resnik, Philip. - 2002 BASE Show details 19 On Minimizing Training Corpus for Parser Acquisition Hwa, Rebecca In: DTIC (2001) Abstract: Many corpus-based natural language processing systems rely on using large quantities of annotated text as their training examples. Building this kind of resource is an expensive and labor-intensive project. To minimize effort spent on annotating examples that are not helpful the training process., recent research efforts have begun to apply active learning techniques to selectively choose data to be annotated. In this work, we consider selecting training examples with the it tree-entropy metric. Our goal is to assess how well this selection technique can be applied for training different types of parsers. We find that tree-entropy can significantly reduce the amount of training annotation for both a history-based parser and an EM-based parser. Moreover, the examples selected for the history-based parser are also good for training the EM-based parser, suggesting that the technique is parser independent. ; Additional report no. UMIACS-TR-2001-40. Supported in part by NSF under Contract IR-9712068 and DARPA under Contract N66991-97-C-8540. Keyword: *PARSERS; ACQUISITION; CORPUS; LEARNING; Linguistics; NATURAL LANGUAGE; TRAINING; TREE-ENTROPY URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458746 http://www.dtic.mil/docs/citations/ADA458746 BASE Hide details 20 Supervised Grammar Induction Using Training Data with Limited Constituent Information ... Hwa, Rebecca. - : arXiv, 1999 BASE Show details Page: 1 2

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