Relevant bibliographies by topics / Query suggestions

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Query suggestions'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Query suggestions"

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Gao, Wei, Cheng Niu, Jian-Yun Nie, Ming Zhou, Kam-Fai Wong, and Hsiao-Wuen Hon. "Exploiting query logs for cross-lingual query suggestions." ACM Transactions on Information Systems 28, no. 2 (2010): 1–33. http://dx.doi.org/10.1145/1740592.1740594.

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Ma, Hao, Michael Lyu, and Irwin King. "Diversifying Query Suggestion Results." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1399–404. http://dx.doi.org/10.1609/aaai.v24i1.7514.

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In order to improve the user search experience, Query Suggestion, a technique for generating alternative queries to Web users, has become an indispensable feature for commercial search engines. However, previous work mainly focuses on suggesting relevant queries to the original query while ignoring the diversity in the suggestions, which will potentially dissatisfy Web users' information needs. In this paper, we present a novel unified method to suggest both semantically relevant and diverse queries to Web users. The proposed approach is based on Markov random walk and hitting time analysis on
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Bonart, Malte, Anastasiia Samokhina, Gernot Heisenberg, and Philipp Schaer. "An investigation of biases in web search engine query suggestions." Online Information Review 44, no. 2 (2019): 365–81. http://dx.doi.org/10.1108/oir-11-2018-0341.

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Purpose Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. Design/methodology/approach This study intro
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Momtazi, Saeedeh, and Fabian Lindenberg. "Generating query suggestions by exploiting latent semantics in query logs." Journal of Information Science 42, no. 4 (2015): 437–48. http://dx.doi.org/10.1177/0165551515594723.

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Kruschwitz, Udo, Deirdre Lungley, M.-Dyaa Albakour, and Dawei Song. "Deriving query suggestions for site search." Journal of the American Society for Information Science and Technology 64, no. 10 (2013): 1975–94. http://dx.doi.org/10.1002/asi.22901.

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Klungre, Vidar N., Ahmet Soylu, Ernesto Jimenez-Ruiz, Evgeny Kharlamov, and Martin Giese. "Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing." New Generation Computing 37, no. 4 (2019): 361–92. http://dx.doi.org/10.1007/s00354-019-00071-1.

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Pillai, Anuradha. "Query Recommendation System in Social Networks." Journal of Management and Service Science (JMSS) 2, no. 2 (2022): 1–20. http://dx.doi.org/10.54060/jmss.2022.20.

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Recommender Systems are software which provides suggestions to the user according to his or her interest. These suggestions are related to supporting users in making their decisions, for example, what to search, what to buy, what to listen, etc. Recommender systems are very important in online stores where there are a lot of items to buy. These recommender systems help user to find things according to their interest and buy them. There are a lot of techniques proposed for recommendation and used in commercial environments. People are thought to trust suggestions from friends more than those fr
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Niu, Xi, and Diane Kelly. "The use of query suggestions during information search." Information Processing & Management 50, no. 1 (2014): 218–34. http://dx.doi.org/10.1016/j.ipm.2013.09.002.

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Chen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantica
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Halimah Tus Sadiah, Lia Dahlia Iryani, Tjut Awaliyah Zuraiyah, Yuli Wahyuni, and Cantika Zaddana. "Implementation of Levenshtein Distance Algorithm for Product Search Query Suggestions on Koro Pedang Edutourism E-Commerce." Journal of Advanced Research in Applied Sciences and Engineering Technology 42, no. 2 (2024): 188–96. http://dx.doi.org/10.37934/araset.42.2.188196.

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Users sometimes write queries that are inaccurate or typos in the product search contained in the Koro Pedang Educational Tourism e-commerce, so the system is not find product search results because the query entered in the system is incorrect. This can frustrate users because they cannot find the product they are looking for, so the users leave the website. According to these problems, it is necessary to suggest a query on the product search function. This is expected to assist users in finding the product they are looking for if there is an error in typing the query. This research purposes w
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Dissertations / Theses on the topic "Query suggestions"

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Plansangket, Suthira. "New weighting schemes for document ranking and ranked query suggestion." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/19456/.

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Term weighting is a process of scoring and ranking a term’s relevance to a user’s information need or the importance of a term to a document. This thesis aims to investigate novel term weighting methods with applications in document representation for text classification, web document ranking, and ranked query suggestion. Firstly, this research proposes a new feature for document representation under the vector space model (VSM) framework, i.e., class specific document frequency (CSDF), which leads to a new term weighting scheme based on term frequency (TF) and the newly proposed feature. The
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Li, Sheng. "Query Suggestion for Keyword Search over XML and RDF Data." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/367139.

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With the growing amount of XML and RDF data, keyword search over XML and RDF data has become an important and increasingly researched topic. In this thesis, we investigate several problems related to keyword search over XML and RDF data, and provide solutions to these problems. The research consists largely of three main technical contributions: top-k nearest keyword (NK) search for XML data, query suggestion for XML data and query suggestion for RDF data. We first study the top-k NK search problem for XML data, which provides an approach to exploring XML queries by the distance
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Qumsiyeh, Rani Majed. "Easy to Find: Creating Query-Based Multi-Document Summaries to Enhance Web Search." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2713.

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Current web search engines, such as Google, Yahoo!, and Bing, rank the set of documents S retrieved in response to a user query Q and display each document with a title and a snippet, which serves as an abstract of the corresponding document in S. Snippets, however, are not as useful as they are designed for, i.e., to assist search engine users to quickly identify results of interest, if they exist, without browsing through the documents in S, since they (i) often include very similar information and (ii) do not capture the main content of the corresponding documents. Moreover, when the intend
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Movin, Maria. "Spelling Correction in a Music Entity Search Engine by Learning from Historical Search Queries." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229716.

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Query spelling correction is an important component of modern search engines that can help users to express their intent, and thus improve search quality. In this study, we investigated with what accuracy a sequence-to-sequence recurrent neural network (RNN) can recognise and correct misspellings in a music search engine, when the model is trained with old search queries. A sequence-to-sequence RNN was chosen as the model in this study since it has achieved state-of-the-art performance on similar tasks, such as machine translation and speech recognition. The findings from the study imply that
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Zhang, Xiaomin. "Search Term Selection and Document Clustering for Query Suggestion." Master's thesis, 2011. http://hdl.handle.net/10048/1680.

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In order to improve a user's query and help the user quickly satisfy his/her information need, most search engines provide query suggestions that are meant to be relevant alternatives to the user's query. This thesis builds on the query suggestion system and evaluation methodology described in Shen Jiang's Masters thesis (2008). Jiang's system constructs query suggestions by searching for lexical aliases of web documents and then applying query search to the lexical aliases. A lexical alias for a web document is a list of terms that return the web document in a top-ranked position. Query searc
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Ke, Yen-Yu, and 柯彥宇. "Modeling and Analyzing User Reformulation Behavior for Query Suggestion." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/87664979571677209070.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>100<br>Query suggestion is an important and widely studied research task in information retrieval. Most previous methods focus on providing suggestions relevant to the single query that the user just submits. Recently, the ‘context’ during the search process is taken into consideration, which includes the previous queries and click information in the same search session. Most of these methods make use of query dependency in the logs and provide suggested queries for particular context. However, a large scale of logs is needed for these methods. Besides, they are not
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Shun-Chen, Cheng, and 鄭舜宸. "Two-level Query Suggestion for Specialization on Web Search Results." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/19941692478611556436.

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碩士<br>國立臺灣師範大學<br>資訊工程學系<br>102<br>The goal of this thesis is to automatically suggest query keywords from the search results returned by the search engine in order to further filter the large amount of search results by using these query keywords as the specialized queries. A two-level query suggestion method, called the M_PhRank, is proposed. The first level suggestion aims to provide the query terms, which can cover search results as many as possible, and the query terms in the second level should have clear meaning and lower overlap between their covered objects. Firstly, the coverage over
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Hung, Cheng-Li, and 洪承理. "A User Query Expansion Behavior Study: Using MeSH as Term Suggestion Source." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71323729282794559136.

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碩士<br>國立臺灣大學<br>圖書資訊學研究所<br>98<br>It has been argued that traditional information retrieval evaluation is ill-equipped to address the need to validate the efficacy of today’s highly interactive systems, which require users’ active participation to be effective. To answer the challenge of interactive information retrieval evaluation, a novel methodology was applied to test the effectiveness of MAP (Multiple Access to PubMed), a metadata-guided search interface for PubMed bibliographic search. The most distinctive aspect of our methodology is to use real users searching for real search requests
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Lin, Ming-Shun, and 林敏順. "A Study on Web-based Relatedness Measure and Its Applications on Community Chain Detection and Query Suggestion." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/34776814134019566803.

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博士<br>國立臺灣大學<br>資訊工程學研究所<br>97<br>In statistical natural language processing, resources used to compute the statistics are indispensable. Different kinds of corpora have made available and many language models have been experimented. One major issue behind the corpus-based approaches is: if corpora adopted can reflect the up-to-date usage. As we know, languages are live. New terms and phrases are used in daily life. How to capture the new usages is an important research topic. This thesis defines a novel web-based relatedness measure and explores snippets in various web domains as cor
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Sordoni, Alessandro. "Learning representations for Information Retrieval." Thèse, 2016. http://hdl.handle.net/1866/13966.

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La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère
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Books on the topic "Query suggestions"

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Gunkel, David J. Can machines have rights? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0063.

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One of the enduring concerns of ethics is determining who is deserving of moral consideration. Although initially limited to “other men,” ethics has developed in such a way that it challenges its own restrictions and comes to encompass what had been previously excluded entities. Currently, we stand on the verge of another fundamental challenge to moral thinking. This challenge comes from the autonomous and increasingly intelligent machines of our own making, and it puts in question many deep-seated assumptions about who or what can be a moral subject. This chapter examines whether machines can
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Book chapters on the topic "Query suggestions"

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Meij, Edgar, Marc Bron, Laura Hollink, Bouke Huurnink, and Maarten de Rijke. "Learning Semantic Query Suggestions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04930-9_27.

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Kapetanios, E., and P. Groenewoud. "Query Construction through Meaningful Suggestions of Terms." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36109-x_18.

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Sejal, D., K. G. Shailesh, V. Tejaswi, et al. "Query Click and Text Similarity Graph for Query Suggestions." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21024-7_22.

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Wan, Kong-Wah, Ah-Hwee Tan, Joo-Hwee Lim, and Liang-Tien Chia. "Topic Based Query Suggestions for Video Search." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27355-1_28.

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P., Deepak, Sutanu Chakraborti, and Deepak Khemani. "Query Suggestions for Textual Problem Solution Repositories." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36973-5_48.

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Haak, Fabian, and Philipp Schaer. "Perception-Aware Bias Detection for Query Suggestions." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78818-6_12.

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Albakour, M.-Dyaa, Udo Kruschwitz, Nikolaos Nanas, et al. "AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20161-5_60.

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Barua, Jayendra, and Dhaval Patel. "Named Entity Classification Using Search Engine’s Query Suggestions." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56608-5_56.

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Shang, Zhenguo, Jingfei Li, Peng Zhang, Dawei Song, and Benyou Wang. "How Users Select Query Suggestions Under Different Satisfaction States?" In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68699-8_8.

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Guijt, Dirk, and Claudia Hauff. "Using Query-Log Based Collective Intelligence to Generate Query Suggestions for Tagged Content Search." In Engineering the Web in the Big Data Era. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19890-3_12.

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Conference papers on the topic "Query suggestions"

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Nawrot, Ilona, Oskar Gross, Antoine Doucet, and Hannu Toivonen. "Novel Query Suggestions." In the 5th International Workshop. ACM Press, 2014. http://dx.doi.org/10.1145/2663792.2663799.

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Zhong, Jianling, Weiwei Guo, Huiji Gao, and Bo Long. "Personalized Query Suggestions." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2020. http://dx.doi.org/10.1145/3397271.3401331.

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Boldi, Paolo, Francesco Bonchi, Carlos Castillo, Debora Donato, and Sebastiano Vigna. "Query suggestions using query-flow graphs." In the 2009 workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1507509.1507518.

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Kim, Youngho, and W. Bruce Croft. "Diversifying query suggestions based on query documents." In SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2014. http://dx.doi.org/10.1145/2600428.2609467.

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Strizhevskaya, Alisa, Alexey Baytin, Irina Galinskaya, and Pavel Serdyukov. "Actualization of query suggestions using query logs." In the 21st international conference companion. ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188152.

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Kamvar, Maryam, and Shumeet Baluja. "Query suggestions for mobile search." In Proceeding of the twenty-sixth annual CHI conference. ACM Press, 2008. http://dx.doi.org/10.1145/1357054.1357210.

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Feuer, Alan, Stefan Savev, and Javed A. Aslam. "Evaluation of phrasal query suggestions." In the sixteenth ACM conference. ACM Press, 2007. http://dx.doi.org/10.1145/1321440.1321556.

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Bhatia, Sumit, Debapriyo Majumdar, and Prasenjit Mitra. "Query suggestions in the absence of query logs." In the 34th international ACM SIGIR conference. ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010023.

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Medlar, Alan, Jing Li, and Dorota Głowacka. "Query Suggestions as Summarization in Exploratory Search." In CHIIR '21: ACM SIGIR Conference on Human Information Interaction and Retrieval. ACM, 2021. http://dx.doi.org/10.1145/3406522.3446020.

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Lissandrini, Matteo, Davide Mottin, Themis Palpanas, and Yannis Velegrakis. "Graph-Query Suggestions for Knowledge Graph Exploration." In WWW '20: The Web Conference 2020. ACM, 2020. http://dx.doi.org/10.1145/3366423.3380005.

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Reports on the topic "Query suggestions"

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Nallapati, Ramesh, and Chirag Shah. Evaluating the Quality of Query Refinement Suggestions in Information Retrieval. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada454796.

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Xu, Chao, Walter Forkel, Stefan Borgwardt, Franz Baader, and Beihai Zhou. Automatic Translation of Clinical Trial Eligibility Criteria into Formal Queries. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.224.

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Selecting patients for clinical trials is very labor-intensive. Our goal is to develop an automated system that can support doctors in this task. This paper describes a major step towards such a system: the automatic translation of clinical trial eligibility criteria from natural language into formal, logic-based queries. First, we develop a semantic annotation process that can capture many types of clinical trial criteria. Then, we map the annotated criteria to the formal query language. We have built a prototype system based on state-of-the-art NLP tools such as Word2Vec, Stanford NLP tools,
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