| Year |
Citation |
Score |
| 2022 |
Kordjamshidi P, Roth D, Kersting K. Declarative Learning-Based Programming as an Interface to AI Systems. Frontiers in Artificial Intelligence. 5: 755361. PMID 35372833 DOI: 10.3389/frai.2022.755361 |
0.344 |
|
| 2020 |
Sachan M, Dubey A, Hovy EH, Mitchell TM, Roth D, Xing EP. Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks Computational Linguistics. 45: 627-665. DOI: 10.1162/Coli_A_00360 |
0.365 |
|
| 2019 |
Rozovskaya A, Roth D. Grammar Error Correction in Morphologically-Rich Languages: The Case of Russian Transactions of the Association For Computational Linguistics. 7: 1-17. DOI: 10.1162/Tacl_A_00251 |
0.327 |
|
| 2019 |
Song Y, Upadhyay S, Peng H, Mayhew S, Roth D. Toward any-language zero-shot topic classification of textual documents Artificial Intelligence. 274: 133-150. DOI: 10.1016/J.Artint.2019.02.002 |
0.377 |
|
| 2018 |
Roy S, Roth D. Mapping to Declarative Knowledge for Word Problem Solving Transactions of the Association For Computational Linguistics. 6: 159-172. DOI: 10.1162/Tacl_A_00012 |
0.311 |
|
| 2018 |
Tsai CT, Mayhew S, Song Y, Sammons M, Roth D. Illinois CCG LoReHLT 2016 Named Entity Recognition And Situation Frame Systems Machine Translation. 32: 91-103. DOI: 10.1007/S10590-017-9211-5 |
0.396 |
|
| 2017 |
Rozovskaya A, Roth D, Sammons M. Adapting to Learner Errors with Minimal Supervision Computational Linguistics. 43: 723-760. DOI: 10.1162/Coli_A_00299 |
0.344 |
|
| 2016 |
Tsai C, Roth D. Concept Grounding to Multiple Knowledge Bases via Indirect Supervision Transactions of the Association For Computational Linguistics. 4: 141-154. DOI: 10.1162/Tacl_A_00089 |
0.371 |
|
| 2016 |
Wang C, Song Y, Roth D, Zhang M, Han J. World Knowledge as Indirect Supervision for Document Clustering Acm Transactions On Knowledge Discovery From Data. 11: 1-36. DOI: 10.1145/2953881 |
0.391 |
|
| 2015 |
Kordjamshidi P, Roth D, Moens MF. Structured learning for spatial information extraction from biomedical text: bacteria biotopes. Bmc Bioinformatics. 16: 129. PMID 25909637 DOI: 10.1186/S12859-015-0542-Z |
0.399 |
|
| 2015 |
Roy S, Vieira T, Roth D. Reasoning about Quantities in Natural Language Transactions of the Association For Computational Linguistics. 3: 1-13. DOI: 10.1162/Tacl_A_00118 |
0.389 |
|
| 2015 |
Vydiswaran VGV, Zhai C, Roth D, Pirolli P. Overcoming bias to learn about controversial topics Journal of the Association For Information Science and Technology. 66: 1655-1672. DOI: 10.1002/Asi.23274 |
0.35 |
|
| 2014 |
Wang D, Al Amin MT, Abdelzaher T, Roth D, Voss CR, Kaplan LM, Tratz S, Laoudi J, Briesch D. Provenance-assisted classification in social networks Ieee Journal On Selected Topics in Signal Processing. 8: 624-637. DOI: 10.1109/Jstsp.2014.2311586 |
0.314 |
|
| 2014 |
Rivera SJ, Minsker BS, Work DB, Roth D. A text mining framework for advancing sustainability indicators Environmental Modelling and Software. 62: 128-138. DOI: 10.1016/J.Envsoft.2014.08.016 |
0.352 |
|
| 2014 |
Goldwasser D, Roth D. Learning from natural instructions Machine Learning. 94: 205-232. DOI: 10.1007/S10994-013-5407-Y |
0.451 |
|
| 2014 |
Bordes A, Bottou L, Collobert R, Roth D, Weston J, Zettlemoyer L. Introduction to the special issue on learning semantics Machine Learning. 94: 127-131. DOI: 10.1007/S10994-013-5381-4 |
0.45 |
|
| 2013 |
Jindal P, Roth D. Extraction of events and temporal expressions from clinical narratives Journal of Biomedical Informatics. 46. PMID 24022023 DOI: 10.1016/J.Jbi.2013.08.010 |
0.319 |
|
| 2013 |
Jindal P, Roth D. Using domain knowledge and domain-inspired discourse model for coreference resolution for clinical narratives Journal of the American Medical Informatics Association. 20: 356-362. PMID 22781192 DOI: 10.1136/Amiajnl-2011-000767 |
0.343 |
|
| 2013 |
Srikumar V, Roth D. Modeling Semantic Relations Expressed by Prepositions Transactions of the Association For Computational Linguistics. 1: 231-242. DOI: 10.1162/Tacl_A_00223 |
0.369 |
|
| 2012 |
do QX, Roth D. Exploiting the wikipedia structure in local and global classification of taxonomic relations Natural Language Engineering. 18: 235-262. DOI: 10.1017/S1351324912000046 |
0.415 |
|
| 2012 |
Chang M, Ratinov L, Roth D. Structured learning with constrained conditional models Machine Learning. 88: 399-431. DOI: 10.1007/S10994-012-5296-5 |
0.427 |
|
| 2011 |
Mengshoel OJ, Wilkins DC, Roth D. Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks Ieee Transactions On Knowledge and Data Engineering. 23: 235-247. DOI: 10.1109/Tkde.2010.98 |
0.36 |
|
| 2011 |
Mengshoel OJ, Roth D, Wilkins DC. Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks Journal of Automated Reasoning. 46: 103-160. DOI: 10.1007/S10817-010-9170-5 |
0.393 |
|
| 2010 |
Dagan I, Dolan B, Magnini B, Roth D. Recognizing textual entailment: Rational, evaluation and approaches Natural Language Engineering. 16: i-xvii. DOI: 10.1017/S1351324909990209 |
0.401 |
|
| 2010 |
Small K, Roth D. Margin-based active learning for structured predictions International Journal of Machine Learning and Cybernetics. 1: 3-25. DOI: 10.1007/S13042-010-0003-Y |
0.431 |
|
| 2008 |
Punyakanok V, Roth D, Yih W. The importance of syntactic parsing and inference in semantic role labeling Computational Linguistics. 34: 257-287. DOI: 10.1162/Coli.2008.34.2.257 |
0.391 |
|
| 2008 |
Daya E, Roth D, Wintner S. Identifying semitic roots: Machine learning with linguistic constraints Computational Linguistics. 34: 429-448. DOI: 10.1162/Coli.2008.07-002-R1-06-30 |
0.442 |
|
| 2006 |
Li X, Roth D. Learning question classifiers: the role of semantic information Natural Language Engineering. 12: 229-249. DOI: 10.1017/S1351324905003955 |
0.446 |
|
| 2006 |
Mengshoel OJ, Wilkins DC, Roth D. Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering Artificial Intelligence. 170: 1137-1174. DOI: 10.1016/J.Artint.2006.09.003 |
0.325 |
|
| 2005 |
Khardon R, Roth D, Servedio RA. Efficiency versus convergence of Boolean kernels for on-line learning algorithms Journal of Artificial Intelligence Research. 24: 341-356. DOI: 10.1613/Jair.1655 |
0.628 |
|
| 2005 |
Li X, Morie P, Roth D. Semantic integration in text: from ambiguous names to identifiable entities Ai Magazine. 26: 45-58. DOI: 10.1609/Aimag.V26I1.1798 |
0.457 |
|
| 2005 |
Fung P, Roth D. Guest editors introduction: Machine learning in speech and language technologies Machine Learning. 60: 5-9. DOI: 10.1007/S10994-005-1399-6 |
0.423 |
|
| 2004 |
Agarwal S, Awan A, Roth D. Learning to detect objects in images via a sparse, part-based representation. Ieee Transactions On Pattern Analysis and Machine Intelligence. 26: 1475-90. PMID 15521495 DOI: 10.1109/Tpami.2004.108 |
0.365 |
|
| 2002 |
Roth D, Yang MH, Ahuja N. Learning to recognize three-dimensional objects. Neural Computation. 14: 1071-103. PMID 11972908 DOI: 10.1162/089976602753633394 |
0.402 |
|
| 2002 |
Greiner R, Grove AJ, Roth D. Learning cost-sensitive active classifiers Artificial Intelligence. 139: 137-174. DOI: 10.1016/S0004-3702(02)00209-6 |
0.411 |
|
| 2001 |
Chuang JS, Roth D. Gene recognition based on DAG shortest paths. Bioinformatics (Oxford, England). 17: S56-64. PMID 11472993 DOI: 10.1093/Bioinformatics/17.Suppl_1.S56 |
0.306 |
|
| 2001 |
Grove AJ, Roth D. Linear concepts and hidden variables Machine Learning. 42: 123-141. DOI: 10.1023/A:1007655119445 |
0.435 |
|
| 1999 |
Khardon R, Roth D. Learning to reason with a restricted view Machine Learning. 35: 95-116. DOI: 10.1023/A:1007581123604 |
0.444 |
|
| 1999 |
Golding AR, Roth D. A Winnow-Based Approach to Context-Sensitive Spelling Correction Machine Learning. 34: 107-130. DOI: 10.1023/A:1007545901558 |
0.496 |
|
| 1999 |
Khardon R, Mannila H, Roth D. Reasoning with examples: Propositional formulae and database dependencies Acta Informatica. 36: 267-286. DOI: 10.1007/S002360050161 |
0.337 |
|
| 1998 |
Aizenstein H, Blum A, Khardon R, Kushilevitz E, Pitt L, Roth D. On Learning Read- k -Satisfy- j DNF Siam Journal On Computing. 27: 1515-1530. DOI: 10.1137/S0097539794274398 |
0.33 |
|
| 1997 |
Khardon R, Roth D. Learning to reason Journal of the Acm. 44: 697-725. DOI: 10.1145/265910.265918 |
0.481 |
|
| 1997 |
Khardon R, Roth D. Defaults and relevance in model-based reasoning Artificial Intelligence. 97: 169-193. DOI: 10.1016/S0004-3702(97)00044-1 |
0.449 |
|
| 1997 |
Daniels K, Milenkovic V, Roth D. Finding the largest area axis-parallel rectangle in a polygon Computational Geometry: Theory and Applications. 7: 125-148. DOI: 10.1016/0925-7721(95)00041-0 |
0.312 |
|
| 1996 |
Kushilevitz E, Roth D. On learning visual concepts and DNF formulae Machine Learning. 24: 65-85. DOI: 10.1023/A:1018098129371 |
0.416 |
|
| 1996 |
Khardon R, Roth D. Reasoning with models Artificial Intelligence. 87: 187-213. DOI: 10.1016/S0004-3702(96)00006-9 |
0.362 |
|
| 1996 |
Roth D. On the hardness of approximate reasoning Artificial Intelligence. 82: 273-302. DOI: 10.1016/0004-3702(94)00092-1 |
0.387 |
|
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