Year |
Citation |
Score |
2019 |
Hardalov M, Koychev I, Nakov P. Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots Information. 10: 82. DOI: 10.3390/Info10030082 |
0.365 |
|
2019 |
Atanasova P, Nakov P, Màrquez L, Barrón-Cedeño A, Karadzhov G, Mihaylova T, Mohtarami M, Glass J. Automatic Fact-Checking Using Context and Discourse Information Journal of Data and Information Quality. 11: 1-27. DOI: 10.1145/3297722 |
0.389 |
|
2019 |
Barrón-Cedeño A, Jaradat I, Da San Martino G, Nakov P. Proppy: Organizing the news based on their propagandistic content Information Processing & Management. 56: 1849-1864. DOI: 10.1016/J.Ipm.2019.03.005 |
0.378 |
|
2018 |
Mihaylov T, Mihaylova T, Nakov P, Màrquez L, Georgiev GD, Koychev IK. The dark side of news community forums: opinion manipulation trolls Internet Research. 28: 1292-1312. DOI: 10.1108/Intr-03-2017-0118 |
0.311 |
|
2018 |
NAKOV P, MÀRQUEZ L, MOSCHITTI A, MUBARAK H. Arabic community question answering Natural Language Engineering. 25: 5-41. DOI: 10.1017/S1351324918000426 |
0.398 |
|
2017 |
Joty S, Guzmán F, Màrquez L, Nakov P. Discourse Structure in Machine Translation Evaluation Computational Linguistics. 43: 683-722. DOI: 10.1162/Coli_A_00298 |
0.305 |
|
2017 |
Guzmán F, Joty S, Màrquez L, Nakov P. Machine translation evaluation with neural networks Computer Speech & Language. 45: 180-200. DOI: 10.1016/J.Csl.2016.12.005 |
0.335 |
|
2016 |
Wang P, Nakov P, Ng HT. Source Language Adaptation Approaches for Resource-Poor Machine Translation Computational Linguistics. 42: 277-306. DOI: 10.1162/Coli_A_00248 |
0.363 |
|
2015 |
Nakov P. Web as a Corpus: Going beyond the n-gram Communications in Computer and Information Science. 505: 185-228. DOI: 10.1007/978-3-319-25485-2_5 |
0.315 |
|
2013 |
Nakov PI, Hearst MA. Semantic interpretation of noun compounds using verbal and other paraphrases Acm Transactions On Speech and Language Processing. 10. DOI: 10.1145/2483969.2483975 |
0.605 |
|
2013 |
Szpakowicz S, Bond F, Nakov P, Kim SN. On the semantics of noun compounds Natural Language Engineering. 19: 289-290. DOI: 10.1017/S1351324913000090 |
0.43 |
|
2013 |
Nakov P. On the interpretation of noun compounds: Syntax, semantics, and entailment Natural Language Engineering. 19: 291-330. DOI: 10.1017/S1351324913000065 |
0.42 |
|
2012 |
Divoli A, Nakov P, Hearst MA. Do peers see more in a paper than its authors? Advances in Bioinformatics. 2012: 750214. PMID 23227044 DOI: 10.1155/2012/750214 |
0.581 |
|
2012 |
Nakov P, Tou Ng H. Improving statistical machine translation for a resource-poor language using related resource-rich languages Journal of Artificial Intelligence Research. 44: 179-222. DOI: 10.1613/Jair.3540 |
0.372 |
|
2011 |
Kim SN, Nakov P. Large-scale noun compound interpretation using bootstrapping and the web as a corpus Emnlp 2011 - Conference On Empirical Methods in Natural Language Processing, Proceedings of the Conference. 648-658. |
0.335 |
|
2011 |
Nakov P, Ng HT. Translating from morphologically complex languages: A paraphrase-based approach Acl-Hlt 2011 - Proceedings of the 49th Annual Meeting of the Association For Computational Linguistics: Human Language Technologies. 1: 1298-1307. |
0.304 |
|
2011 |
Nakov P, Kozareva Z. Combining relational and attributional similarity for semantic relation classification International Conference Recent Advances in Natural Language Processing, Ranlp. 323-330. |
0.327 |
|
2009 |
Girju R, Nakov P, Nastase V, Szpakowicz S, Turney P, Yuret D. Classification of semantic relations between nominals Language Resources and Evaluation. 43: 105-121. DOI: 10.1007/s10579-009-9083-2 |
0.314 |
|
2009 |
Georgiev G, Nakov P, Ganchev K, Osenova P, Simov K. Feature-rich named entity recognition for bulgarian using conditional random fields International Conference Recent Advances in Natural Language Processing, Ranlp. 113-117. |
0.317 |
|
2008 |
Nakov P. Noun compound interpretation using paraphrasing verbs: Feasibility study Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5253: 103-117. DOI: 10.1007/978-3-540-85776-1_10 |
0.348 |
|
2008 |
Nakov P, Hearst MA. Solving relational similarity problems using the web as a corpus Acl-08: Hlt - 46th Annual Meeting of the Association For Computational Linguistics: Human Language Technologies, Proceedings of the Conference. 452-460. |
0.611 |
|
2007 |
Hearst MA, Divoli A, Guturu H, Ksikes A, Nakov P, Wooldridge MA, Ye J. BioText Search Engine: beyond abstract search. Bioinformatics (Oxford, England). 23: 2196-7. PMID 17545178 DOI: 10.1093/Bioinformatics/Btm301 |
0.614 |
|
2006 |
Divoli A, Hearst MA, Nakov PI, Schwartz A, Ksikes A. Biotext team report for the TREC 2006 genomics track Nist Special Publication. |
0.469 |
|
2006 |
Nakov P, Hearst M. Using verbs to characterize noun-noun relations Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4183: 233-244. |
0.63 |
|
2005 |
Nakov P, Hearst M. A study of using search engine page hits as a proxy for n-gram frequencies International Conference Recent Advances in Natural Language Processing, Ranlp. 2005: 347-353. |
0.566 |
|
2005 |
Nakov P, Hearst M. Using the web as an implicit training set: Application to structural ambiguity resolution Hlt/Emnlp 2005 - Human Language Technology Conference and Conference On Empirical Methods in Natural Language Processing, Proceedings of the Conference. 835-842. |
0.58 |
|
2005 |
Nakov P, Hearst M. Search engine statistics beyond the n-gram: Application to noun compound bracketing Conll 2005 - Proceedings of the Ninth Conference On Computational Natural Language Learning. 17-24. |
0.614 |
|
2005 |
Nakov P, Schwartz A, Wolf B, Hearst M. Supporting annotation layers for natural language processing Acl-05 - 43rd Annual Meeting of the Association For Computational Linguistics, Proceedings of the Conference. 65-68. |
0.569 |
|
2004 |
Angelova G, Strupchanska A, Kalaydjiev O, Yankova M, Boytcheva S, Vitanova I, Nakov P. Towards deeper understanding and personalisation in CALL Elearn. 45-52. DOI: 10.3115/1610028.1610035 |
0.331 |
|
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