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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 Hits 1 – 20 of 53 1 Decontextualization: Making Sentences Stand-Alone ... Choi, Eunsol; Palomaki, Jennimaria; Lamm, Matthew. - : arXiv, 2021 BASE Show details 2 On planetary systems as ordered sequences ... Sandford, Emily; Kipping, David; Collins, Michael. - : arXiv, 2021 BASE Show details 3 Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Collins, Michael; Effland, Thomas. - : Underline Science Inc., 2021 BASE Show details 4 QED: A Framework and Dataset for Explanations in Question Answering ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Eunsol; Alberti, Chris. - : Underline Science Inc., 2021 BASE Show details 5 TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages ... Clark, Jonathan H.; Choi, Eunsol; Collins, Michael. - : arXiv, 2020 BASE Show details 6 QED: A Framework and Dataset for Explanations in Question Answering ... Lamm, Matthew; Palomaki, Jennimaria; Alberti, Chris. - : arXiv, 2020 BASE Show details 7 Natural Questions: A Benchmark for Question Answering Research Kwiatkowski, Tom; Palomaki, Jennimaria; Redfield, Olivia... In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 453-466 (2019) (2019) BASE Show details 8 Cross-Lingual Syntactic Transfer with Limited Resources ... Rasooli, Mohammad Sadegh; Collins, Michael. - : arXiv, 2016 BASE Show details 9 Predicting the impact of scientific concepts using full‐text features McKeown, Kathy; Daume, Hal; Chaturvedi, Snigdha. - : Association for Computational Linguistics, 2016. : Wiley Periodicals, Inc., 2016 BASE Show details 10 Learning Dictionaries for Named Entity Recognition using Minimal Supervision ... Neelakantan, Arvind; Collins, Michael. - : arXiv, 2015 BASE Show details 11 Information Density and Dependency Length as Complementary Cognitive Models Collins, Michael Xavier In: Journal of psycholinguistic research. - New York, NY ; London [u.a.] : Springer 43 (2014) 5, 651-681 OLC Linguistik Show details 12 Experiments With Spectral Learning of Latent-Variable PCFGs Cohen, Shay B; Stratos, Karl; Collins, Michael... In: Statistics Papers (2013) BASE Show details 13 Cognitive Perspectives On English Word Order Collins, Michael Xavier In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1343315752 (2012) BASE Show details 14 They do be anxious about their speech: Performance and Perceptions of Authenticity in Irish-Newfoundland English Collins, Michael In: The English Languages: History, Diaspora, Culture; Vol 3 (2012) ; 1929-5855 (2012) BASE Show details 15 Depth and distance perception of dentists and dental students Dimitrijevic, Teodora; Kahler, Bill; Evans, Gareth. - : Operative Dentistry, 2011 BASE Show details 16 Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel: Presentation Slides Hirschberg, Julia Bell; Biadsy, Fadi; Collins, Michael. - : Interspeech 2010, 2010 BASE Show details 17 Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel Hirschberg, Julia Bell; Biadsy, Fadi; Collins, Michael. - : Interspeech 2010, 2010 Abstract: In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similarities of phones between pairs of utterances. We employ this kernel to train SVM classifiers that estimate posterior probabilities, used during recognition. Testing our approach on four Arabic dialects from 30s cuts, we compare our performance to five approaches: PRLM; GMM-UBM; our own improved version of GMM-UBM which employs fMLLR adaptation; our recent discriminative phonotactic approach; and a state-of-the-art system: SDC-based GMM-UBM discriminatively trained. Our kernel-based technique outperforms all these previous approaches; the overall EER of our system is 4.9%. Keyword: Computer science; Information technology; Linguistics URL: https://doi.org/10.7916/D81J9K8W BASE Hide details 18 Efficient third-order dependency parsers Koo, Terry; Collins, Michael In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 1-11 BLLDB Show details 19 On dual decomposition and linear programming relaxations for natural language processing Rush, Alexander Matthew; Sontag, David Alexander; Collins, Michael... In: MIT web domain (2010) BASE Show details 20 Dual decomposition for parsing with non-projective head automata Koo, Terry; Rush, Alexander Matthew; Collins, Michael... In: MIT web domain (2010) BASE Show details Page: 1 2 3

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