Academic literature on the topic 'FAQ RETRIEVAL'
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Journal articles on the topic "FAQ RETRIEVAL"
Setiawan, Gede Herdian, and I. Made Budi Adnyana. "Information Retrieval Pada Frequently Asked Questions (FAQ) dengan metode String Similarity." Techno.Com 21, no. 4 (November 30, 2022): 847–55. http://dx.doi.org/10.33633/tc.v21i4.6843.
Full textWu, Chung-Hsien, Jui-Feng Yeh, and Ming-Jun Chen. "Domain-specific FAQ retrieval using independent aspects." ACM Transactions on Asian Language Information Processing 4, no. 1 (March 2005): 1–17. http://dx.doi.org/10.1145/1066078.1066079.
Full textThuma, Edwin, Moemedi Lefoane, and Gontlafetse Mosweunyane. "A review on the Detection of Missing Content Queries in FAQ Retrieval Systems." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 2 (April 7, 2017): 6203–6. http://dx.doi.org/10.24297/ijct.v16i2.5996.
Full textHarksoo Kim and Jungyun Seo. "Cluster-Based FAQ Retrieval Using Latent Term Weights." IEEE Intelligent Systems 23, no. 2 (March 2008): 58–65. http://dx.doi.org/10.1109/mis.2008.23.
Full textRomero, M., A. Moreo, and J. L. Castro. "A cloud of FAQ: A highly-precise FAQ retrieval system for the Web 2.0." Knowledge-Based Systems 49 (September 2013): 81–96. http://dx.doi.org/10.1016/j.knosys.2013.04.019.
Full textChung-Hsien Wu, Jui-Feng Yeh, and Yu-Sheng Lai. "Semantic segment extraction and matching for Internet FAQ retrieval." IEEE Transactions on Knowledge and Data Engineering 18, no. 7 (July 2006): 930–40. http://dx.doi.org/10.1109/tkde.2006.115.
Full textMoreo, A., E. M. Eisman, J. L. Castro, and J. M. Zurita. "Learning regular expressions to template-based FAQ retrieval systems." Knowledge-Based Systems 53 (November 2013): 108–28. http://dx.doi.org/10.1016/j.knosys.2013.08.018.
Full textAhn, Hyeokju, and Harksoo Kim. "Enhanced Spoken Sentence Retrieval Using a Conventional Automatic Speech Recognizer in Smart Home." International Journal on Artificial Intelligence Tools 25, no. 03 (June 2016): 1650017. http://dx.doi.org/10.1142/s0218213016500172.
Full textMoreo, A., M. Navarro, J. L. Castro, and J. M. Zurita. "A high-performance FAQ retrieval method using minimal differentiator expressions." Knowledge-Based Systems 36 (December 2012): 9–20. http://dx.doi.org/10.1016/j.knosys.2012.05.015.
Full textMoreo, A., M. Romero, J. L. Castro, and J. M. Zurita. "FAQtory: A framework to provide high-quality FAQ retrieval systems." Expert Systems with Applications 39, no. 14 (October 2012): 11525–34. http://dx.doi.org/10.1016/j.eswa.2012.02.130.
Full textDissertations / Theses on the topic "FAQ RETRIEVAL"
Thuma, Edwin. "A semi-automated FAQ retrieval system for HIV/AIDS." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6280/.
Full textRizzo, Carlo Anthony Edward. "Phase retrieval near-field/far-field measurement techniques for quasi-optical large apertures." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310888.
Full textJAIN, MUKUL. "N-GRAM DRIVEN SMS BASED FAQ RETRIEVAL SYSTEM." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14128.
Full textMEENA, SONAL. "SMS BASED FAQ RETRIEVAL USING HYBRID SIMILARITY MEASURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14443.
Full textKuen-Lin, Lee, and 李坤霖. "Intention Extraction and Semantic Matching for Internet FAQ Retrieval." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/64357868695480318793.
Full text國立成功大學
資訊工程研究所
88
Internet FAQ(frequently asked questions) retrieval provides a way for information retrieval based on natural language query. Each FAQ consists of a question and a text document that answers the question. As a query is similar to the FAQ’s question, the FAQ’s answer gives a possible answer or parts of the answer of the query. In this thesis, a method for question similarity matching is proposed to interpret the natural language query. By analyzing a query sentence, a keyword segment and an intention segment can be extracted. The sentence similarity is obtained by combining the similarity scores for these two segments. In intention similarity, we proposed a semantic matching with parse tree structure. In word-to-word similarity is adopted for both intention and keyword segment similarity estimation. A Chinese word knowledge base, “How-net”, provides the basic knowledge for word-to-word similarity measure. Finally, a VSM(vector space model) full-text retrieval which is widely used in keyword based retrieval is integrated into the system as the similarity from query sentence to FAQ’s answers. In order to evaluate the system performance, a collection of 1017 FAQ patterns and a set of 203 query sentences are collected for experiment. In intention extraction, 92% of intention segments can be extracted correctly. The average rank of correct answers is improved from 12.04 to 2.91.And the recall rate of 95.11% for the top 10 FAQ patterns and an improvement of 27.05%.
Wang, Hai-Sia, and 王海霞. "The Study of Applying Concept Map on FAQ Retrieval." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/55170221509135470776.
Full text輔仁大學
資訊管理學系
97
It is a conventional way using keywords on information retrieval. The disadvantages are information overloading, ambiguous query terms and imprecise queries. So far, domestic FAQ websites and the related literatures on FAQ retrieval mostly have used keywords and natural language for information retrieval. Because of the different expressions of users, it could have cognitive differences between users and systems so that users could not find any information. Therefore, using concept maps to represent FAQ questions with visualization for searching relevant questions will improve the disadvantages of using keywords and natural language. The purpose of this study is to apply the method of concept map for FAQ question retrieval. First, extracting the keywords to represent the questions by using k-means clustering algorithm for question clustering. Second, using association rules to produce concept rules in each question cluster. Finally, connecting concept rules to form a concept map. The measures of precision, recall and F-measure are used to evaluate the results of question clustering. The representativeness of concept maps is evaluated by computing the values of precision, recall and F-measure and also compared with the results of using keywords. The experiments of this study show that the results of using concept maps on information retrieval are not very significant, however, the performance on precision and recall is relatively higher than using keyword retrieval. Moreover, there is one problem with questions belonging to two clusters in this study in the collection of Taipei City Mayor Mail data and Chunghwa Telecom FAQ data. This problem is because artificially classified questions are not appropriate. This study proposes a new suggestion on classification. We explore the characteristics of two different data and the methods to be used in order to have better searching results.
Kuo, Chung-Han, and 郭忠漢. "FAQ Like Chinese Natural Language Information Retrieval Based on SQL." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/33093824648289828823.
Full text國立暨南國際大學
資訊管理學系
89
Recently, there are many museums building their digital museums because of the popularity of the World Wide Web. It not only makes users to get information by the Internet but also makes museums to store their literatures in other storages. The Orchid Island Digital Museum is such a digital museum. Its literatures were collecting from Orchid Island by experts. It uses XML documents and SQL Server 2000 to store its literatures, and each of them has its properties. The Orchid Island Digital Museum uses their properties to manage its literatures and to present them on the World Wide Web. So, it will provide such function as Digital Book Reservation, Digital Presentation, and Remote Education, etc. in the future. Our information retrieval system builds on the Orchid Island Digital Museum. And our system also uses their properties to increase system''s precision. Our information retrieval system provides an FAQ like query interface. It lets users construct their information need more conveniently. After users key-in their information need, then our system uses Chinese text segmentation that base on dictionary to translate their sentences to query that our system can be manipulated. Then uses Structure Query Language to support our system to analyze user''s information need. Finally, our system extracts some documents that fit in with user''s need from the Orchid Island Digital Museum database.
Lai, Yu-Sheng, and 賴育昇. "A Study on Natural Language Processing for Internet FAQ Retrieval." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/swxkt8.
Full text國立成功大學
資訊工程學系碩博士班
90
Open-domain question answering is a new challenge to both commercial applications and academic research. From the viewpoint of answer generation, the research on question answering can be approached in two ways: (1) generating new answers and (2) seeking answers from a vast amount of data collection. AI experts tend to focus on the generation of new answers. This kind of approaches is now available only for developing domain-specific systems. On the other hand, the data collection for seeking answers can be subdivided into two types: question-dissociated and question-associated types. In the data collection, all text data can be viewed as question-dissociated answers. On the contrary, the question-associated answers, such as frequently asked questions (FAQ), represent that each possible answer refers to at least one question. For today’s technology, FAQ retrieval is more feasible to answer open-domain questions. However, there are many problems to overcome still. Data collection, i.e. FAQ collection, is the first problem. How do you collect enough FAQ files for user requirements? Many Websites create and maintain FAQ pages for customer service, advertisement, etc. For various styles of Web papers, this dissertation proposes a language-independent approach to mining FAQ from the WWW. Moreover, a large amount of FAQs should be classified appropriately to shorten the search time and to improve the accuracy. Therefore, the technology of text categorization is another topic we are interested. The generation of new words is usually for describing specific events, personages, etc. or to be proper nouns. In other words, they usually occur in a few specific domains more frequently than many other domains. Utilizing the characteristic of unknown words, we improve the performance of traditional text classifiers basing on characters, words, and N-grams. Unlike Western languages, there are no any delimiters between written words for Chinese and most Asia languages. For these languages, unknown word problem is a serious problem. Based on the assumption that unknown words consist of words and undefined characters, this dissertation proposes a statistical approach to unknown word detection. We propose a formula to measure the likelihood that a string is an unknown word, and an algorithm to detect unknown words in sentences. FAQ retrieval is based on an intuition that if a user query is semantically similar to the questions of some question-answer pairs, then the corresponding answers of the question-answer pairs are possibly answers to the user query. Therefore, this dissertation also probes into the comparison of two questions in semantics. By the analysis of Chinese question types, we create a set of semantic grammar. Cooperating a partial parser of Chinese questions, a question is parsed into to two parts: an intention segment and a string of keywords. The intention segment is defined as “a combination of component segments in a question that conveys the surface purpose, and need not to comprise other auxiliary or functional clauses.” By comparing their intention segments and keyword string respectively and combining their weighted results, we can obtain the result of question comparison.
Hong, Yu-Rong, and 洪郁融. "Ontology- and Retrieval-based Trademark Consultation Chatbot – The Case of Taiwan Trademark Services FAQ." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/z37ja2.
Full textAdesina, Ademola Olusola. "Short message service normalization for communication with a health information system." Thesis, 2011. http://hdl.handle.net/11394/3594.
Full textShort Message Service (SMS) is one of the most popularly used services for communication between mobile phone users. In recent times it has also been proposed as a means for information access. However, there are several challenges to be overcome in order to process an SMS, especially when it is used as a query in an information retrieval system.SMS users often tend deliberately to use compacted and grammatically incorrect writing that makes the message difficult to process with conventional information retrieval systems. To overcome this, a pre-processing step known as normalization is required. In this thesis an investigation of SMS normalization algorithms is carried out. To this end,studies have been conducted into the design of algorithms for translating and normalizing SMS text. Character-based, unsupervised and rule-based techniques are presented. An investigation was also undertaken into the design and development of a system for information access via SMS. A specific system was designed to access information related to a Frequently Asked Questions (FAQ) database in healthcare, using a case study. This study secures SMS communication, especially for healthcare information systems. The proposed technique is to encipher the messages using the secure shell (SSH) protocol.
Books on the topic "FAQ RETRIEVAL"
Saffady, William. Film-based imaging in the document life cycle: FAQs for best practices. Silver Spring, Md: AIIM International, 2001.
Find full textPing, Fu, and Liu Xiaoling, eds. Zhong wen shu mu gui fan kong zhi de li lun yu shi jian. Beijing: Beijing tu shu guan chu ban she, 2007.
Find full textXingyuan, Huang, and Chen Bingxian, eds. Sheng, shi, xian ju yu gui hua yu guan li xin xi xi tong gui fan hua yan jiu. Nanjing: Nanjing da xue chu ban she, 1991.
Find full textFar-East Workshop on Geographic Information Systems. (1993 Singapore). GIS: Technology and applications : proceedings of the Far East Workshop on Geographic Information Systems, Singapore, 21-22 June 1993. Edited by Lu Hung-chün and Ooi Beng Chin 1961-. Singapore: World Scientific, 1993.
Find full textSvec, Henry Adam. American Folk Music as Tactical Media. NL Amsterdam: Amsterdam University Press, 2017. http://dx.doi.org/10.5117/9789462984943.
Full textWen xian xin xi jian suo yu li yong. Beijing: Ke xue ji shu wen xian chu ban she, 2003.
Find full textPapanicolaou, Andrew C., Nicole Shay, and Christen M. Holder. Imaging the Networks of Encoding, Consolidation, and Retrieval. Edited by Andrew C. Papanicolaou. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.21.
Full textMcGrath, Alister E. Natural Philosophy. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192865731.001.0001.
Full textEriksson, Olle, Anders Bergman, Lars Bergqvist, and Johan Hellsvik. Ultrafast Switching Dynamics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788669.003.0011.
Full textShe hui ke xue wen xian jian suo yu li yong (Gao deng shi fan yuan xiao tu shu guan li yong cong shu). Ba Shu shu she, 1990.
Find full textBook chapters on the topic "FAQ RETRIEVAL"
Shivhre, Nishit. "SMS Based FAQ Retrieval." In Multilingual Information Access in South Asian Languages, 131–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_13.
Full textSinghal, Khushboo, Gaurav Arora, Smita Kumari, and Prasenjit Majumder. "SMS Normalization for FAQ Retrieval." In Multilingual Information Access in South Asian Languages, 163–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_16.
Full textPakray, Partha, Santanu Pal, Soujanya Poria, Sivaji Bandyopadhyay, and Alexander Gelbukh. "SMSFR: SMS-Based FAQ Retrieval System." In Advances in Computational Intelligence, 36–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37798-3_4.
Full textMakino, Takuya, Tomoya Noro, Hiyori Yoshikawa, Tomoya Iwakura, Satoshi Sekine, and Kentaro Inui. "A FAQ Search Training Method Based on Automatically Generated Questions." In Information Retrieval Technology, 67–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03520-4_7.
Full textKim, Harksoo, Hyunjung Lee, and Jungyun Seo. "Improving FAQ Retrieval Using Query Log Clustering in Latent Semantic Space." In Information Retrieval Technology, 233–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_18.
Full textDe, Arijit. "SMS Based FAQ Retrieval Using Latent Semantic Indexing." In Multilingual Information Access in South Asian Languages, 100–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_10.
Full textBhattacharya, Sanmitra, Hung Tran, and Padmini Srinivasan. "Data-Driven Methods for SMS-Based FAQ Retrieval." In Multilingual Information Access in South Asian Languages, 104–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_11.
Full textShaikh, Anwar D., Mukul Jain, Mukul Rawat, Rajiv Ratn Shah, and Manoj Kumar. "Improving Accuracy of SMS Based FAQ Retrieval System." In Multilingual Information Access in South Asian Languages, 142–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_14.
Full textHogan, Deirdre, Johannes Leveling, Hongyi Wang, Paul Ferguson, and Cathal Gurrin. "SMS Normalisation, Retrieval and Out-of-Domain Detection Approaches for SMS-Based FAQ Retrieval." In Multilingual Information Access in South Asian Languages, 184–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_18.
Full textMogadala, Aditya, Rambhoopal Kothwal, and Vasudeva Varma. "Language Modeling Approach to Retrieval for SMS and FAQ Matching." In Multilingual Information Access in South Asian Languages, 119–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40087-2_12.
Full textConference papers on the topic "FAQ RETRIEVAL"
Gupta, Sparsh, and Vitor R. Carvalho. "FAQ Retrieval Using Attentive Matching." In SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3331184.3331294.
Full textKothari, Govind, Sumit Negi, Tanveer A. Faruquie, Venkatesan T. Chakaravarthy, and L. Venkata Subramaniam. "SMS based interface for FAQ retrieval." In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1690219.1690266.
Full textChen, Zhiyu, Jason Choi, Besnik Fetahu, Oleg Rokhlenko, and Shervin Malmasi. "Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-industry.73.
Full textBatra, Ruhi, Sanchit Sharma, Anurag Shrivastav, and Puneet Goyal. "Efficiently denoising SMS text for FAQ retrieval." In 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC). IEEE, 2014. http://dx.doi.org/10.1109/icdmic.2014.6954237.
Full textLeveling, Johannes. "Monolingual and Crosslingual SMS-based FAQ Retrieval." In the 5th 2013 Forum. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/2701336.2701634.
Full textLiu, Lu, Qifei Wu, and Guang Chen. "Improving Dense FAQ Retrieval with Synthetic Training." In 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC). IEEE, 2021. http://dx.doi.org/10.1109/ic-nidc54101.2021.9660603.
Full textAyalew, Yirsaw, Gontlafetse Mosweunyane, and Barbara Moeng. "An Ontology-based HIV/AIDS FAQ Retrieval System." In Environment and Water Resource Management. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.815-008.
Full textZhang, Leilei, and Junfei Liu. "Curriculum Contrastive Learning for COVID-19 FAQ Retrieval." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995534.
Full textMass, Yosi, Boaz Carmeli, Haggai Roitman, and David Konopnicki. "Unsupervised FAQ Retrieval with Question Generation and BERT." In 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.74.
Full textMlambo, Godfrey, and Yirsaw Ayalew. "Intelligent HIV/AIDS FAQ Retrieval System using Neural Networks." In Environment and Water Resource Management. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.815-012.
Full textReports on the topic "FAQ RETRIEVAL"
Forbus, Kenneth D., Dedre Gentner, and Keith Law. MAC/FAC: A Model of Similarity-Based Retrieval. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada286291.
Full textForbus, Kenneth D., Dedre Gentner, and Keith Law. MAC/FAC: A Model of Similarity-Based Retrieval. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada288515.
Full textBlumwald, Eduardo, and Avi Sadka. Citric acid metabolism and mobilization in citrus fruit. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7587732.bard.
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