Literatura académica sobre el tema "Extractive Question-Answering"
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Artículos de revistas sobre el tema "Extractive Question-Answering"
Xu, Marie-Anne y Rahul Khanna. "Evaluation of Single-Span Models on Extractive Multi-Span Question-Answering". International journal of Web & Semantic Technology 12, n.º 1 (31 de enero de 2021): 19–29. http://dx.doi.org/10.5121/ijwest.2021.12102.
Texto completoGuan, Yue, Zhengyi Li, Zhouhan Lin, Yuhao Zhu, Jingwen Leng y Minyi Guo. "Block-Skim: Efficient Question Answering for Transformer". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 10 (28 de junio de 2022): 10710–19. http://dx.doi.org/10.1609/aaai.v36i10.21316.
Texto completoHu, Zhongjian, Peng Yang, Bing Li, Yuankang Sun y Biao Yang. "Biomedical extractive question answering based on dynamic routing and answer voting". Information Processing & Management 60, n.º 4 (julio de 2023): 103367. http://dx.doi.org/10.1016/j.ipm.2023.103367.
Texto completoOuyang, Jianquan y Mengen Fu. "Improving Machine Reading Comprehension with Multi-Task Learning and Self-Training". Mathematics 10, n.º 3 (19 de enero de 2022): 310. http://dx.doi.org/10.3390/math10030310.
Texto completoShinoda, Kazutoshi, Saku Sugawara y Akiko Aizawa. "Which Shortcut Solution Do Question Answering Models Prefer to Learn?" Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 11 (26 de junio de 2023): 13564–72. http://dx.doi.org/10.1609/aaai.v37i11.26590.
Texto completoLongpre, Shayne, Yi Lu y Joachim Daiber. "MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering". Transactions of the Association for Computational Linguistics 9 (2021): 1389–406. http://dx.doi.org/10.1162/tacl_a_00433.
Texto completoGholami, Sia y Mehdi Noori. "You Don’t Need Labeled Data for Open-Book Question Answering". Applied Sciences 12, n.º 1 (23 de diciembre de 2021): 111. http://dx.doi.org/10.3390/app12010111.
Texto completoLI, SHASHA y ZHOUJUN LI. "QUESTION-ORIENTED ANSWER SUMMARIZATION VIA TERM HIERARCHICAL STRUCTURE". International Journal of Software Engineering and Knowledge Engineering 21, n.º 06 (septiembre de 2011): 877–89. http://dx.doi.org/10.1142/s0218194011005475.
Texto completoMoon, Sungrim, Huan He, Heling Jia, Hongfang Liu y Jungwei Wilfred Fan. "Extractive Clinical Question-Answering With Multianswer and Multifocus Questions: Data Set Development and Evaluation Study". JMIR AI 2 (20 de junio de 2023): e41818. http://dx.doi.org/10.2196/41818.
Texto completoSiblini, Wissam, Mohamed Challal y Charlotte Pasqual. "Efficient Open Domain Question Answering With Delayed Attention in Transformer-Based Models". International Journal of Data Warehousing and Mining 18, n.º 2 (abril de 2022): 1–16. http://dx.doi.org/10.4018/ijdwm.298005.
Texto completoTesis sobre el tema "Extractive Question-Answering"
Bergkvist, Alexander, Nils Hedberg, Sebastian Rollino y Markus Sagen. "Surmize: An Online NLP System for Close-Domain Question-Answering and Summarization". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412247.
Texto completoMängden data som är tillgänglig och konsumeras av människor växer globalt. För att minska den mentala trötthet och öka den allmänna förmågan att få insikt i komplexa, massiva texter eller dokument, har vi utvecklat en applikation för att bistå i de uppgifterna. Applikationen tillåter användare att ladda upp dokument och fråga kontextspecifika frågor via vår webbapplikation. En sammanfattad version av varje dokument presenteras till användaren, vilket kan ytterligare förenkla förståelsen av ett dokument och vägleda dem mot vad som kan vara relevanta frågor att ställa. Vår applikation ger användare möjligheten att behandla olika typer av dokument, är tillgänglig för alla, sparar ingen personlig data, och använder de senaste modellerna inom språkbehandling för dess sammanfattningar och svar. Resultatet är en applikation som når en nära mänsklig intuition för vissa domäner och frågor, som exempelvis Wikipedia- och nyhetsartiklar, samt viss vetensaplig text. Noterade undantag för tillämpningen härrör från ämnets komplexitet, grammatiska korrekthet för frågorna och dokumentets längd. Dessa är områden som kan förbättras ytterligare om den används i produktionen.
Usbeck, Ricardo. "Knowledge Extraction for Hybrid Question Answering". Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-225097.
Texto completoGlinos, Demetrios. "SYNTAX-BASED CONCEPT EXTRACTION FOR QUESTION ANSWERING". Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3565.
Texto completoPh.D.
School of Computer Science
Engineering and Computer Science
Computer Science
Mur, Jori. "Off-line answer extraction for question answering". [S.l. : [Groningen : s.n.] ; University Library Groningen] [Host], 2008. http://irs.ub.rug.nl/ppn/.
Texto completoKonstantinova, Natalia. "Knowledge acquisition from user reviews for interactive question answering". Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/297401.
Texto completoAlmansa, Luciana Farina. "Uma arquitetura de question-answering instanciada no domínio de doenças crônicas". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-10102016-121606/.
Texto completoThe medical record describes health conditions of patients helping experts to make decisions about the treatment. The biomedical scientific knowledge can improve the prevention and the treatment of diseases. However, the search for relevant knowledge may be a hard task because it is necessary time and the healthcare research is constantly updating. Many healthcare professionals have a stressful routine, because they work in different hospitals or medical offices, taking care many patients per day. The goal of this project is to design a Question Answering Framework to support faster and more precise searches for information in epigenetic, chronic disease and thyroid images. To develop the proposal, we are reusing two frameworks that have already developed: SisViDAS and FREDS. These two frameworks are being exploited to compose a document processing module. The other modules (question and answer processing) are being completely developed. The QASF was evaluated by a reference collection and performance measures. The results show 0.7 of precision and 0.3 of recall for two hundred articles retrieved. Considering that the questions inserted on the framework have an average of seventy terms, the QASF shows good results. This project intends to decrease search time once QA systems provide straight and precise answers in a process started by a user question in natural language
Usbeck, Ricardo [Verfasser], Klaus-Peter [Gutachter] Fähnrich, Philipp [Gutachter] Cimiano, Ngomo Axel-Cyrille [Akademischer Betreuer] Ngonga y Klaus-Peter [Akademischer Betreuer] Fähnrich. "Knowledge Extraction for Hybrid Question Answering / Ricardo Usbeck ; Gutachter: Klaus-Peter Fähnrich, Philipp Cimiano ; Akademische Betreuer: Axel-Cyrille Ngonga Ngomo, Klaus-Peter Fähnrich". Leipzig : Universitätsbibliothek Leipzig, 2017. http://d-nb.info/1173734775/34.
Texto completoKrč, Martin. "Znalec encyklopedie". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236707.
Texto completoDeyab, Rodwan Bakkar. "Ontology-based information extraction from learning management systems". Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/20996.
Texto completoBen, Abacha Asma. "Recherche de réponses précises à des questions médicales : le système de questions-réponses MEANS". Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00735612.
Texto completoLibros sobre el tema "Extractive Question-Answering"
Harabagiu, Sanda y Dan Moldovan. Question Answering. Editado por Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0031.
Texto completoPazienza, Maria Teresa. Information Extraction in the Web Era: Natural Language Communication for Knowledge Acquisition and Intelligent Information Agents. Springer London, Limited, 2006.
Buscar texto completoInformation Extraction in the Web Era: Natural Language Communication for Knowledge Acquisition and Intelligent Information Agents (Lecture Notes in Computer Science). Springer, 2003.
Buscar texto completoCapítulos de libros sobre el tema "Extractive Question-Answering"
Jha, Raj y V. Susheela Devi. "Extractive Question Answering Using Transformer-Based LM". En Communications in Computer and Information Science, 373–84. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1642-9_32.
Texto completoFerreira, Bruno Carlos Luís, Hugo Gonçalo Oliveira, Hugo Amaro, Ângela Laranjeiro y Catarina Silva. "Evaluating the Extraction of Toxicological Properties with Extractive Question Answering". En Engineering Applications of Neural Networks, 599–606. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34204-2_48.
Texto completoKabber, Anusha, V. M. Dhruthi, Raghav Pandit y S. Natarajan. "Extractive Long-Form Question Answering for Annual Reports Using BERT". En Proceedings of Emerging Trends and Technologies on Intelligent Systems, 295–304. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4182-5_23.
Texto completoDu, Mingzhe, Mouad Hakam, See-Kiong Ng y Stéphane Bressan. "Constituency-Informed and Constituency-Constrained Extractive Question Answering with Heterogeneous Graph Transformer". En Transactions on Large-Scale Data- and Knowledge-Centered Systems LIII, 90–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66863-4_4.
Texto completoSang, Erik Tjong Kim, Katja Hofmann y Maarten de Rijke. "Extraction of Hypernymy Information from Text∗". En Interactive Multi-modal Question-Answering, 223–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17525-1_10.
Texto completoBouma, Gosse, Ismail Fahmi y Jori Mur. "Relation Extraction for Open and Closed Domain Question Answering". En Interactive Multi-modal Question-Answering, 171–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17525-1_8.
Texto completovan der Plas, Lonneke, Jörg Tiedemann y Ismail Fahmi. "Automatic Extraction of Medical Term Variants from Multilingual Parallel Translations". En Interactive Multi-modal Question-Answering, 149–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17525-1_7.
Texto completoSrihari, Rohini K., Wei Li y Xiaoge Li. "Question Answering Supported By Multiple Levels Of Information Extraction". En Advances in Open Domain Question Answering, 349–82. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-4746-6_11.
Texto completoLehmann, Jens, Tim Furche, Giovanni Grasso, Axel-Cyrille Ngonga Ngomo, Christian Schallhart, Andrew Sellers, Christina Unger et al. "deqa: Deep Web Extraction for Question Answering". En The Semantic Web – ISWC 2012, 131–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35173-0_9.
Texto completoKontos, J. y I. Malagardi. "Question Answering and Information Extraction from Texts". En Advances in Intelligent Systems, 121–30. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4840-5_11.
Texto completoActas de conferencias sobre el tema "Extractive Question-Answering"
Wang, Luqi, Kaiwen Zheng, Liyin Qian y Sheng Li. "A Survey of Extractive Question Answering". En 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS). IEEE, 2022. http://dx.doi.org/10.1109/hdis56859.2022.9991478.
Texto completoShymbayev, Magzhan y Yermek Alimzhanov. "Extractive Question Answering for Kazakh Language". En 2023 IEEE International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2023. http://dx.doi.org/10.1109/sist58284.2023.10223508.
Texto completoFajcik, Martin, Josef Jon y Pavel Smrz. "Rethinking the Objectives of Extractive Question Answering". En Proceedings of the 3rd Workshop on Machine Reading for Question Answering. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.mrqa-1.2.
Texto completoArumae, Kristjan y Fei Liu. "Guiding Extractive Summarization with Question-Answering Rewards". En Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1264.
Texto completoLewis, Patrick, Barlas Oguz, Ruty Rinott, Sebastian Riedel y Holger Schwenk. "MLQA: Evaluating Cross-lingual Extractive Question Answering". En Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.653.
Texto completoPrasad, Archiki, Trung Bui, Seunghyun Yoon, Hanieh Deilamsalehy, Franck Dernoncourt y Mohit Bansal. "MeetingQA: Extractive Question-Answering on Meeting Transcripts". En Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.837.
Texto completoRakotoson, Loïc, Charles Letaillieur, Sylvain Massip y Fréjus A. A. Laleye. "Extractive-Boolean Question Answering for Scientific Fact Checking". En ICMR '22: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3512732.3533580.
Texto completoVaranasi, Stalin, Saadullah Amin y Guenter Neumann. "AutoEQA: Auto-Encoding Questions for Extractive Question Answering". En Findings of the Association for Computational Linguistics: EMNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-emnlp.403.
Texto completoFrermann, Lea. "Extractive NarrativeQA with Heuristic Pre-Training". En Proceedings of the 2nd Workshop on Machine Reading for Question Answering. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5823.
Texto completoDemner-Fushman, Dina y Jimmy Lin. "Answer extraction, semantic clustering, and extractive summarization for clinical question answering". En the 21st International Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1220175.1220281.
Texto completoInformes sobre el tema "Extractive Question-Answering"
Srihari, Rohini y Wei Li. Information Extraction Supported Question Answering. Fort Belvoir, VA: Defense Technical Information Center, octubre de 1999. http://dx.doi.org/10.21236/ada460042.
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