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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 Findings of the IWSLT 2020 Evaluation campaign Niehues, Jan; Federico, Marcello; Ma, Xutai. - : Association for Computational Linguistics, 2022 BASE Show details 2 MeetDot: Videoconferencing with Live Translation Captions ... Arkhangorodsky, Arkady; Chu, Christopher; Fang, Scot. - : arXiv, 2021 BASE Show details 3 MeetDot: Videoconferencing with Live Translation Captions ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Arkhangorodsky, Arkady; Chu, Christopher. - : Underline Science Inc., 2021 BASE Show details 4 Abstract Meaning Representation (AMR) Annotation Release 3.0 Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020 BASE Show details 5 Parallel Corpus Filtering via Pre-trained Language Models ... Zhang, Boliang; Nagesh, Ajay; Knight, Kevin. - : arXiv, 2020 BASE Show details 6 Findings of the IWSLT 2020 Evaluation campaign ... Ansari, Ebrahim; Axelrod, Amittai; Bach, Nguyen. - : Association for Computational Linguistics, 2020 BASE Show details 7 Abstract Meaning Representation (AMR) Annotation Release 3.0 ... Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020 BASE Show details 8 Learning to Pronounce Chinese Without a Pronunciation Dictionary ... Chu, Christopher; Fang, Scot; Knight, Kevin. - : arXiv, 2020 BASE Show details 9 Cross-lingual entity extraction and linking for 300 languages Pan, Xiaoman. - 2020 BASE Show details 10 Multi-Source Attention for Unsupervised Domain Adaptation. Bollegala, Danushka; Cui, Xia. - : Association for Computational Linguistics, 2020 BASE Show details 11 Neighbors helping the poor: improving low-resource machine translation using related languages [<Journal>] Pourdamghani, Nima [Verfasser]; Knight, Kevin [Verfasser] DNB Subject Category Language Show details 12 Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling ... Shivakumar, Prashanth Gurunath; Li, Haoqi; Knight, Kevin. - : arXiv, 2018 BASE Show details 13 Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding ... Huang, Lifu; Cho, Kyunghyun; Zhang, Boliang. - : arXiv, 2018 BASE Show details 14 Abstract Meaning Representation (AMR) Annotation Release 2.0 Knight, Kevin; Badarau, Bianca; Baranescu, Laura; Bonial, Claire; Bardocz, Madalina; Griffitt, Kira; Hermjakob, Ulf; Marcu, Daniel; Palmer, Martha; O'Gorman, Tim; Schneider, Nathan. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017 Abstract: *Introduction* Abstract Meaning Representation (AMR) Annotation Release 2.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 39,260 English natural language sentences from broadcast conversations, newswire, weblogs and web discussion forums. AMR captures “who is doing what to whom” in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax. LDC also released Abstract Meaning Representation (AMR) Annotation Release 1.0 (LDC2014T12). *Data* The source data includes discussion forums collected for the DARPA BOLT and DEFT programs, transcripts and English translations of Mandarin Chinese broadcast news programming from China Central TV, Wall Street Journal text, translated Xinhua news texts, various newswire data from NIST OpenMT evaluations and weblog data used in the DARPA GALE program. The following table summarizes the number of training, dev, and test AMRs for each dataset in the release. Totals are also provided by partition and dataset: Dataset Training Dev Test Totals BOLT DF MT 1061 133 133 1327 Broadcast conversation 214 0 0 214 Weblog and WSJ 0 100 100 200 BOLT DF English 6455 210 229 6894 DEFT DF English 19558 0 0 19558 Guidelines AMRs 819 0 0 819 2009 Open MT 204 0 0 204 Proxy reports 6603 826 823 8252 Weblog 866 0 0 866 Xinhua MT 741 99 86 926 Totals 36521 1368 1371 39260 For those interested in utilizing a standard/community partition for AMR research (for instance in development of semantic parsers), data in the "split" directory contains 39,260 AMRs split roughly 93%/3.5%/3.5% into training/dev/test partitions, with most smaller datasets assigned to one of the splits as a whole. Note that splits observe document boundaries. The "unsplit" directory contains the same 39,260 AMRs with no train/dev/test partition. *Samples* Please view this sample. *Updates* None at this time. *Acknowledgements* From University of Colorado We gratefully acknowledge the support of the National Science Foundation Grant NSF: 0910992 IIS:RI: Large: Collaborative Research: Richer Representations for Machine Translation and the support of Darpa BOLT - HR0011-11-C-0145 and DEFT - FA-8750-13-2-0045 via a subcontract from LDC. 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 National Science Foundation, DARPA or the US government. From Information Sciences Institute (ISI) Thanks to NSF (IIS-0908532) for funding the initial design of AMR, and to DARPA MRP (FA-8750-09-C-0179) for supporting a group to construct consensus annotations and the AMR Editor. The initial AMR bank was built under DARPA DEFT FA-8750-13-2-0045 (PI: Stephanie Strassel; co-PIs: Kevin Knight, Daniel Marcu, and Martha Palmer) and DARPA BOLT HR0011-12-C-0014 (PI: Kevin Knight). From Linguistic Data Consortium (LDC) This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government. We gratefully acknowledge the support of Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0184 Subcontract 4400165821. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the DARPA, AFRL, or the US government. From Language Weaver (SDL) This work was partially sponsored by DARPA contract HR0011-11-C-0150 to LanguageWeaver Inc. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the DARPA or the US government. URL: https://catalog.ldc.upenn.edu/LDC2017T10 BASE Hide details 15 Abstract Meaning Representation (AMR) Annotation Release 2.0 ... Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017 BASE Show details 16 Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features Wiegand, Michael [Verfasser]; Schulder, Marc [Verfasser]; Ruppenhofer, Josef [Verfasser]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2016 DNB Subject Category Language Show details 17 Opinion Holder and Target Extraction on Opinion Compounds – A Linguistic Approach Wiegand, Michael [Verfasser]; Bocionek, Christine [Verfasser]; Ruppenhofer, Josef [Verfasser]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2016 DNB Subject Category Language Show details 18 Leadership discourse as basis and means for developing L2 students into future leaders Knight, Kevin; Candlin, Chris. - : Reading, UK : Garnet Publishing, 2015 BASE Show details 19 Analysing the discourses of leadership as a basis for developing leadership communication skills in a second or foreign language Knight, Kevin. - : Sydney, Australia : Macquarie University, 2015 BASE Show details 20 Statistical Techniques for Translating to Morphologically Rich Languages (Dagstuhl Seminar 14061) Koehn, Philipp; Uszkoreit, Hans; Schmid, Helmut. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2014. : Dagstuhl Reports. Dagstuhl Reports, Volume 4, Issue 2, 2014 BASE Show details Page: 1 2 3

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 6 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 3 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 11 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 35 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0