Дисертації з теми "Extractive Question-Answering"
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Bergkvist, Alexander, Nils Hedberg, Sebastian Rollino, and 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.
Повний текст джерелаMängden 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.
Повний текст джерелаGlinos, 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.
Повний текст джерелаPh.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/.
Повний текст джерелаKonstantinova, Natalia. "Knowledge acquisition from user reviews for interactive question answering." Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/297401.
Повний текст джерелаAlmansa, 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/.
Повний текст джерелаThe 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, and 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.
Повний текст джерелаKrč, Martin. "Znalec encyklopedie." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236707.
Повний текст джерелаDeyab, Rodwan Bakkar. "Ontology-based information extraction from learning management systems." Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/20996.
Повний текст джерелаBen, 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.
Повний текст джерела"Knowledge Representation, Reasoning and Learning for Non-Extractive Reading Comprehension." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.55482.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Computer Science 2019
Usbeck, Ricardo. "Knowledge Extraction for Hybrid Question Answering." Doctoral thesis, 2016. https://ul.qucosa.de/id/qucosa%3A15647.
Повний текст джерелаChih-Tai, Huang. "Chinese Information Extraction and Question Answering System based on Relational Conceptual Schema." 2003. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611285897.
Повний текст джерелаHuang, Chih-Tai, and 黃志泰. "Chinese Information Extraction and Question Answering System based on Relational Conceptual Schema." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/26226243243271106147.
Повний текст джерела元智大學
資訊工程學系
92
Traditional Chinese text retrieval systems return a ranked list of documents in response to a user’s request. While a ranked list of documents can be an appropriate response for the user, frequently it is not. Usually it would be better for the system to provide the answer itself instead of requiring the user to search for the answer in a set of documents. Since Chinese text retrieval has just been developed lately, and due to various specific characteristics of Chinese language, the approaches of its retrieval are quite different from those studies and researches proposed to deal with Western language. Thus, we proposed a document characterization model- EAVR, to solve the Chinese text retrieval problem. In the EAVR conceptual model, an information index structure that satisfies the requirements of information retrieval or information extraction is established during context analysis stage. Besides, the new type of concept tree allows document characteristics schema to be reorganized and reconstructed with the concept tree. We also developed an architecture that augments existing search engines so that they support Chinese natural language question answering. In this dissertation we describe a new approach to build Chinese question answering system, which we believe to be the first general-purpose, fully-automated Chinese question-answering system available on the web. In our approach, we attempt to represent Chinese text by its characteristics, and try to convert the Chinese text into ERE (E: entity, R: relation) relation data lists, and then, to answer the question through ERE relation model. Our system performs quite well in addition to the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching ERE relation data lists. Simple ERE relation data extraction techniques work well in our system making it efficient to use with many backend retrieval engines.
Zhang, Zhuo. "Domain-specific question answering system : an application to the construction sector." Thèse, 2003. http://hdl.handle.net/1866/14526.
Повний текст джерела