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Academic literature on the topic 'FAQ RETRIEVAL'

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Published: 11 September 2023

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Journal articles on the topic "FAQ RETRIEVAL"

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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.

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Information retrieval merupakan sebuah sarana untuk menemukan informasi berdasarkan kumpulan informasi pada data terstruktur maupun tidak terstruktur secara otomatis. implementasi information retrival seperti mesin pencari menggunakan query dari pengguna dengan bahasa alami manusia kemudian sistem dapat menemukan dokumen atau informasi yang berkaitan dengan query dari pengguna. Pada penelitian ini di usulkan sistem information retrieval pada Frequently Asked Questions atau FAQ dengan mencari pertanyaan yang mirip (similar) pada daftar pertanyaan di basis data terhadap pertanyaan yang diberikan oleh pengguna menggunakan algoritma Cosine similarity untuk mencari kesamaan kosinus tertinggi. Selanjutnya memberikan respon jawaban yang sebelum nya sudah di berikan label terhadap pertanyaan yang relevan dan memiliki similaritas paling tinggi. Telah dihasilkan dataset FAQ dan dilakukan preprocessing, penerapan algoritma Cosine Similarity terhadap input pertanyaan (query) dengan dataset dan menghasilkan bobot pada setiap pertanyaan (label) pada dataset. Melalui evaluasi akurasi pemberian bobot similaritas yang dilakukan dengan memberikan sembilan input pertanyaan dibagai pada tiga kategori berdasarkan tingkat kemiripan memiliki akurasi mencapai 100%, dengan demikian information retrieval dengan Cosine similarity telah mampu memberikan bobot sesuai dengan tingkat similaritas pertanyaan (query) dengan dataset pertanyaan pada FAQ
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Wu, 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.

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Thuma, 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.

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When developing an automated FAQ retrieval system, the information supplier constructs question candidates in advance using their own knowledge. Then they answer these question candidates to create question-answer pairs to use in the FAQ retrieval system. However, these question-answer pairs will not always satisfy the users’ information needs. When there is no relevant question–answer pair to a users’ query, such a user may submit various query reformulations browsing over the long results list and may abandon the search before their information need has been satisfied. Such users many never return to use the system again because of the inability of the system to return relevant question-answer pairs to their query. In order to alleviate this, modern automated FAQ retrieval systems use a Missing Content Query (MCQ) detection subsystem to detect those queries that do not have the relevant question–answer pair. In this article we conduct a review of the different approaches proposed in the literature for detecting these MCQs. In particular, we provide a comprehensive review of the different systems that deployed the binary classification approach, the thresholding approach and the hybrid approach in the detection of MCQs. Moreover, we describe the strength and weaknesses of each approach.
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Harksoo 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.

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Romero, 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.

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Chung-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.

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Moreo, 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.

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Ahn, 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.

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With the rapid evolution of smart home environment, the demand for spoken information retrieval (e.g., voice-activated FAQ retrieval) on information appliances is increasing. In spoken information retrieval, users’ spoken queries are converted into text queries using automatic speech recognition (ASR) engines. If top-1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a document collection is a small set of sentences such as frequently asked questions (FAQs), the errors have additional effect on the performance of information retrieval systems. To improve the performance of such a sentence retrieval system, we propose a post-processing model of an ASR engine. The post-processing model consists of a re-ranking and a query term generation model. The re-ranking model rearranges top-n outputs of the ASR engines using the ranking support vector machine (Ranking SVM). The query term generation model extracts meaningful content words from the re-ranked queries based on term frequencies and query rankings. In the experiments, the re-ranking model improved the top-1 performance results of an underlying ASR engine with 4.4% higher precision and 6.4% higher recall rate. The query term generation model improved the performance results of an underlying information retrieval system with an accuracy 2.4% to 2.6% higher. Based on the experimental result, the proposed model revealed that it could improve the performance of a spoken sentence retrieval system in a restricted domain.
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Moreo, 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.

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Moreo, 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.

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Dissertations / Theses on the topic "FAQ RETRIEVAL"

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Thuma, Edwin. "A semi-automated FAQ retrieval system for HIV/AIDS." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6280/.

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This thesis describes a semi-automated FAQ retrieval system that can be queried by users through short text messages on low-end mobile phones to provide answers on HIV/AIDS related queries. First we address the issue of result presentation on low-end mobile phones by proposing an iterative interaction retrieval strategy where the user engages with the FAQ retrieval system in the question answering process. At each iteration, the system returns only one question-answer pair to the user and the iterative process terminates after the user's information need has been satisfied. Since the proposed system is iterative, this thesis attempts to reduce the number of iterations (search length) between the users and the system so that users do not abandon the search process before their information need has been satisfied. Moreover, we conducted a user study to determine the number of iterations that users are willing to tolerate before abandoning the iterative search process. We subsequently used the bad abandonment statistics from this study to develop an evaluation measure for estimating the probability that any random user will be satisfied when using our FAQ retrieval system. In addition, we used a query log and its click-through data to address three main FAQ document collection deficiency problems in order to improve the retrieval performance and the probability that any random user will be satisfied when using our FAQ retrieval system. Conclusions are derived concerning whether we can reduce the rate at which users abandon their search before their information need has been satisfied by using information from previous searches to: Address the term mismatch problem between the users' SMS queries and the relevant FAQ documents in the collection; to selectively rank the FAQ document according to how often they have been previously identified as relevant by users for a particular query term; and to identify those queries that do not have a relevant FAQ document in the collection. In particular, we proposed a novel template-based approach that uses queries from a query log for which the true relevant FAQ documents are known to enrich the FAQ documents with additional terms in order to alleviate the term mismatch problem. These terms are added as a separate field in a field-based model using two different proposed enrichment strategies, namely the Term Frequency and the Term Occurrence strategies. This thesis thoroughly investigates the effectiveness of the aforementioned FAQ document enrichment strategies using three different field-based models. Our findings suggest that we can improve the overall recall and the probability that any random user will be satisfied by enriching the FAQ documents with additional terms from queries in our query log. Moreover, our investigation suggests that it is important to use an FAQ document enrichment strategy that takes into consideration the number of times a term occurs in the query when enriching the FAQ documents. We subsequently show that our proposed enrichment approach for alleviating the term mismatch problem generalise well on other datasets. Through the evaluation of our proposed approach for selectively ranking the FAQ documents, we show that we can improve the retrieval performance and the probability that any random user will be satisfied when using our FAQ retrieval system by incorporating the click popularity score of a query term t on an FAQ document d into the scoring and ranking process. Our results generalised well on a new dataset. However, when we deploy the click popularity score of a query term t on an FAQ document d on an enriched FAQ document collection, we saw a decrease in the retrieval performance and the probability that any random user will be satisfied when using our FAQ retrieval system. Furthermore, we used our query log to build a binary classifier for detecting those queries that do not have a relevant FAQ document in the collection (Missing Content Queries (MCQs))). Before building such a classifier, we empirically evaluated several feature sets in order to determine the best combination of features for building a model that yields the best classification accuracy in identifying the MCQs and the non-MCQs. Using a different dataset, we show that we can improve the overall retrieval performance and the probability that any random user will be satisfied when using our FAQ retrieval system by deploying a MCQs detection subsystem in our FAQ retrieval system to filter out the MCQs. Finally, this thesis demonstrates that correcting spelling errors can help improve the retrieval performance and the probability that any random user will be satisfied when using our FAQ retrieval system. We tested our FAQ retrieval system with two different testing sets, one containing the original SMS queries and the other containing the SMS queries which were manually corrected for spelling errors. Our results show a significant improvement in the retrieval performance and the probability that any random user will be satisfied when using our FAQ retrieval system.
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Rizzo, 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.

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JAIN, MUKUL. "N-GRAM DRIVEN SMS BASED FAQ RETRIEVAL SYSTEM." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14128.

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In the present scenario, everyone is looking for a better, efficient and easy way to access information. Resource availability and its user friendliness are directly encouraging a large group of people to access information more conveniently. Short Messaging Service (SMS) is one of the most popularly used services that provide information access to the people having mobile phones. In India alone, there are around 811 million mobile subscribers and still growing with a fast rate [3]. So, SMS based Question Answering (QA) services can be one of the cheapest and easiest ways to provide information access to the mobile users on move. However, there are several significant challenges in order to process a SMS query automatically. Humans have tendency to use abbreviations and shortcuts in their SMSes. We call these inconsistencies as noise in the SMSes. Existing SMS services such as service to access Examination result requires user to type the message in some specific format. These are the unnecessarily constraints to the users who generally feel it convenient to type a query in a “texting” language (i.e. including abbreviations and the shortcuts). Some businesses such as “ChaCha” [5] allow their users to make query through the SMSes without using any specific format. But these services are not automatic and the SMSes are handled by human experts. Though this kind of system provides independence to the users in writing the SMS query but this approach is not an efficient way to handle user‟s queries because the system is limited to handle a small number of queries proportional to the number of human experts on the business side. The approach can be efficient if we have a system which automatically handles the query at business side. In this thesis work, I presented a novel approach to handle these inconsistencies in the SMSes efficiently. This approach for SMS based FAQ retrieval system took N-gram counts into consideration while calculating the score for the question in the corpus. The experimental results demonstrates that this approach is significantly improves the accuracy of previous SMS based QA system as proposed by L. Venkata Subramaniam et al., August 2009 [1]. I demonstrate my algorithm over many real-life FAQ-datasets from different domains (e.g. Agriculture, Bank, Health, Insurance and Telecom etc.).
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MEENA, SONAL. "SMS BASED FAQ RETRIEVAL USING HYBRID SIMILARITY MEASURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14443.

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Mobile technology gave contribution to the progress of media of communication for example: chats, emails and short message services (SMS). The Popularity, utility and simplicity of SMSes is encouraging people to access information via SMSes, accessing information via internet creates hassle, it’s not necessary that internet connection is always available. So user can clarify their query, make complaint and get updates of result etc by sending SMS. Accessing information in such a manner makes information access very economic and easy for everybody from rural to metro city people. “FAQ retrieval” means there is corpora of frequently asked questions, and user sends a query in SMS language to retrieve some information. Such systems finds best match from FAQ corpora for given user defined query written in SMS language. The main problem in SMS language is the noise associated with it. Spelling mistakes, transliteration, phonetic spellings, abbreviations and short forms create difficulties in string matching. In proposed work, a novel approach has been presented by developing Hybrid similarities which evaluates similarity scores with the questions in the corpus for SMS query. In this way, we can further improve the accuracy of the SMS based FAQ system significantly by refining the results of the system using different hybrid similarity scores.
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Kuen-Lin, Lee, and 李坤霖. "Intention Extraction and Semantic Matching for Internet FAQ Retrieval." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/64357868695480318793.

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碩士
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%.
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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.

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碩士
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.
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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.

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碩士
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.
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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.

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博士
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.
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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.

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Adesina, Ademola Olusola. "Short message service normalization for communication with a health information system." Thesis, 2011. http://hdl.handle.net/11394/3594.

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Philosophiae Doctor - PhD
Short 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.
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Books on the topic "FAQ RETRIEVAL"

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Saffady, William. Film-based imaging in the document life cycle: FAQs for best practices. Silver Spring, Md: AIIM International, 2001.

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Ping, 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.

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Xingyuan, 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.

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Far-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.

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Svec, Henry Adam. American Folk Music as Tactical Media. NL Amsterdam: Amsterdam University Press, 2017. http://dx.doi.org/10.5117/9789462984943.

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American folk music has long presented a problematic conception of authenticity, but the reality of the folk scene, and its relationship to media, is far more complicated. This book draws on the fields of media archaeology, performance studies, and sound studies to explore the various modes of communication that can be uncovered from the long American folk revival. From Alan Lomax's cybernetic visions to Bob Dylan's noisy writing machines, this book retrieves a subterranean discourse on the concept of media that might help us to reimagine the potential of the networks in which we work, play, and sing.
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Wen xian xin xi jian suo yu li yong. Beijing: Ke xue ji shu wen xian chu ban she, 2003.

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Papanicolaou, 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.

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In this chapter, the authors examine the contributions of the functional neuroimaging literature to the specification of the neuronal networks of the mnemonic operations of encoding, consolidation, and retrieval. Although the most basic expectation regarding the involvement of parts of the medial temporal lobes, such as the hippocampus, in these operations was not consistently supported by the results of the neuroimaging studies reviewed, other expectations, such as the material-specific lateralization of activation were adequately supported. The several reasons that account for the limited contributions of neuroimaging to the neurophysiology of memory thus far, ranging from constraints imposed by the nature of the mnemonic operations (e.g., the fact that encoding and retrieval occur in tandem) to practical ones (e.g., difficulties in studying spontaneous retrieval), are outlined.
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McGrath, Alister E. Natural Philosophy. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192865731.001.0001.

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Abstract In the seventeenth century, natural philosophy was seen as an integrated enterprise, embracing what are now seen as separate disciplines, such as philosophy, the natural sciences, mathematics, and theology. Although often portrayed as a now redundant precursor of the natural sciences, natural philosophy was far more than this, enfolding the two quite different notions of learning about and learning from nature. This book argues for the retrieval of the ‘disciplinary imaginary’ of natural philosophy. The first part of the work explores how this idea emerged in the writings of Aristotle, and achieved its greatest influence in the seventeenth century. It offers a critical conversation with leading representatives of the movement—such as Johann Kepler, Robert Boyle, and Isaac Newton—to clarify its scope and significance, as well as identifying the factors causing the decline of the movement in the nineteenth century. The second part of the book sets out a comprehensive account of how natural philosophy can be retrieved and reimagined. Drawing on recent discussions of progress in philosophy, it argues that a retrieved natural philosophy can hold together both the objective and subjective aspects of the human engagement with the natural world. Using Mary Midgley’s approach to safeguarding the complexity of nature and Karl Popper’s model of the ‘three worlds’ of human knowledge—objective, subjective, and theoretical—the book offers a comprehensive vision of the scope of a revitalized natural philosophy, and the benefits this brings to the human understanding and appreciation of nature.
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Eriksson, 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.

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The time-integrated amount of data and stored information, is doubled roughly every eighteen months, and since the majority of the worlds information is stored in magnetic media, the possibility to write and retrieve information in a magnetic material at ever greater speed and with lower energy consumption, has obvious benefits for our society. Hence the seemingly simple switching of a magnetic unit, a bit, is a crucial process which defines how efficiently information can be stored and retrieved from a magnetic memory. Of particular interest here are the concepts of ultrafast magnetism and all-optical control of magnetism which have in recent decades become the basis for an intense research field. The motivation is natural; the mechanisms behind these phenomena are far from trivial and the technological implications are huge.
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She 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.

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Book chapters on the topic "FAQ RETRIEVAL"

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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.

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Singhal, 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.

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Pakray, 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.

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Makino, 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.

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Kim, 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.

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De, 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.

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Bhattacharya, 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.

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Shaikh, 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.

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Hogan, 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.

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Mogadala, 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.

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Conference papers on the topic "FAQ RETRIEVAL"

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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.

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Kothari, 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.

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Chen, 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.

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Batra, 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.

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Leveling, 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.

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Liu, 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.

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Ayalew, 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.

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Zhang, 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.

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Mass, 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.

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Mlambo, 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.

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Reports on the topic "FAQ RETRIEVAL"

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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.

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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/ada288515.

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Blumwald, 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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Accumulation of citric acid is a major determinant of maturity and fruit quality in citrus. Many citrus varieties accumulate citric acid in concentrations that exceed market desires, reducing grower income and consumer satisfaction. Citrate is accumulated in the vacuole of the juice sac cell, a process that requires both metabolic changes and transport across cellular membranes, in particular, the mitochondrial and the vacuolar (tonoplast) membranes. Although the accumulation of citrate in the vacuoles of juice cells has been clearly demonstrated, the mechanisms for vacuolar citrate homeostasis and the components controlling citrate metabolism and transport are still unknown. Previous results in the PIs’ laboratories have indicated that the expression of a large number of a large number of proteins is enhanced during fruit development, and that the regulation of sugar and acid content in fruits is correlated with the differential expression of a large number of proteins that could play significant roles in fruit acid accumulation and/or regulation of acid content. The objectives of this proposal are: i) the characterization of transporters that mediate the transport of citrate and determine their role in uptake/retrieval in juice sac cells; ii) the study of citric acid metabolism, in particular the effect of arsenical compounds affecting citric acid levels and mobilization; and iii) the development of a citrus fruit proteomics platform to identify and characterize key processes associated with fruit development in general and sugar and acid accumulation in particular. The understanding of the cellular processes that determine the citrate content in citrus fruits will contribute to the development of tools aimed at the enhancement of citrus fruit quality. Our efforts resulted in the identification, cloning and characterization of CsCit1 (Citrus sinensis citrate transporter 1) from Navel oranges (Citrus sinesins cv Washington). Higher levels of CsCit1 transcripts were detected at later stages of fruit development that coincided with the decrease in the juice cell citrate concentrations (Shimada et al., 2006). Our functional analysis revealed that CsCit1 mediates the vacuolar efflux of citrate and that the CsCit1 operates as an electroneutral 1CitrateH2-/2H+ symporter. Our results supported the notion that it is the low permeable citrateH2 - the anion that establishes the buffer capacity of the fruit and determines its overall acidity. On the other hand, it is the more permeable form, CitrateH2-, which is being exported into the cytosol during maturation and controls the citrate catabolism in the juice cells. Our Mass-Spectrometry-based proteomics efforts (using MALDI-TOF-TOF and LC2- MS-MS) identified a large number of fruit juice sac cell proteins and established comparisons of protein synthesis patterns during fruit development. So far, we have identified over 1,500 fruit specific proteins that play roles in sugar metabolism, citric acid cycle, signaling, transport, processing, etc., and organized these proteins into 84 known biosynthetic pathways (Katz et al. 2007). This data is now being integrated in a public database and will serve as a valuable tool for the scientific community in general and fruit scientists in particular. Using molecular, biochemical and physiological approaches we have identified factors affecting the activity of aconitase, which catalyze the first step of citrate catabolism (Shlizerman et al., 2007). Iron limitation specifically reduced the activity of the cytosolic, but not the mitochondrial, aconitase, increasing the acid level in the fruit. Citramalate (a natural compound in the juice) also inhibits the activity of aconitase, and it plays a major role in acid accumulation during the first half of fruit development. On the other hand, arsenite induced increased levels of aconitase, decreasing fruit acidity. We have initiated studies aimed at the identification of the citramalate biosynthetic pathway and the role(s) of isopropylmalate synthase in this pathway. These studies, especially those involved aconitase inhibition by citramalate, are aimed at the development of tools to control fruit acidity, particularly in those cases where acid level declines below the desired threshold. Our work has significant implications both scientifically and practically and is directly aimed at the improvement of fruit quality through the improvement of existing pre- and post-harvest fruit treatments.
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