Relevant bibliographies by topics / Keywords extraction

Contents

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

Academic literature on the topic 'Keywords extraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Keywords extraction"

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Sun, Changjian, Wentao Chen, Zhen Zhang, and Tian Zhang. "A Patent Keyword Extraction Method Based on Corpus Classification." Mathematics 12, no. 7 (2024): 1068. http://dx.doi.org/10.3390/math12071068.

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The keyword extraction of patents is crucial for technicians to master the trends of technology. Traditional keyword extraction approaches only handle short text like title or claims, but ignore the comprehensive meaning of the description. This paper proposes a novel patent keyword extraction method based on corpus classification (PKECC), which simulates the patent understanding methods of human patent examiners. First of all, a corpus classification model based on multi-level attention mechanism adopts the Bert model and hierarchical attention mechanism to classify the sentences of patent de
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Ahmad, Jawad, Abdur Rehman, Hafiz Tayyab Rauf, Kashif Javed, Maram Abdullah Alkhayyal, and Abeer Ali Alnuaim. "Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria." Applied Sciences 12, no. 7 (2022): 3544. http://dx.doi.org/10.3390/app12073544.

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Businesses are overgrowing worldwide; people struggle for their businesses and startups in almost every field of life, whether industrial or academic. The businesses or services have multiple income streams with which they generate revenue. Most companies use different marketing and advertisement strategies to engage their customers and spread their services worldwide. Service recommendation systems are gaining popularity to recommend the best services and products to customers. In recent years, the development of service-oriented computing has had a significant impact on the growth of busines
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Yoshikawa, Tomohiro, Yuki Uchida, Takeshi Furuhashi, Eiji Hirao, and Hiroto Iguchi. "Extraction of Evaluation Keywords for Analyzing Product Evaluation in User-Reviews Using Hierarchical Keyword Graph." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (2009): 457–62. http://dx.doi.org/10.20965/jaciii.2009.p0457.

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Recently, the number of sites on the Internet which give users the opportunity to write their ideas and opinions for the public to read have been increasing. In addition, the number of people who want to know the opinions of others about interesting products has also been increasing. However, it is very difficult for people to read complete reviews on the Internet. This study tries to develop a new system for the analysis of reviews, a system which shows evaluation information about products using graphs of evaluation keywords. This paper focuses on the extraction of evaluation keywords from r
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Li, Ya Min, and Xian Huan Zhang. "The Research and Implementation of Keyword Extraction Technology." Applied Mechanics and Materials 644-650 (September 2014): 2003–8. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2003.

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Keyword extraction plays an important role in abstract, information retrieval, data mining, text clustering etc. Extracting the keywords from a document can increases the efficiency of retrieval, thus provide great help to efficiently organize the resource. Few writers on the Internet have given the keywords of a document. Artificially extracting the keywords of a document is a great deal of work, so we need a method of extracting the keywords automatically. The paper constructing a verb, function words, stop words etc. small library from the perspective of the Chinese part of speech, realize
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Son. "AUTOMATIC KEYWORD EXTRACTION USING ARTIFICIAL NEURAL NETWORK AND FEATURE EXTRACTION." Journal of Military Science and Technology, no. 69A (November 16, 2020): 63–74. http://dx.doi.org/10.54939/1859-1043.j.mst.69a.2020.63-74.

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Extracting keywords from documents is an essential task in natural language processing. A challenge of this task is to define a reasonable set of keywords from which we can find all relevant documents. This paper proposes a new approach that exploits word-level handcrafted features and machine learning models to select a single document's most important keywords. To evaluate the proposed solution, we compare our results with the latest supervised and unsupervised automatic keyword extraction methods. Experiment results show that our model achieves the best results on the 9/20 data corpus. It p
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Miah, M. Saef Ullah, Junaida Sulaiman, Talha Bin Sarwar, Kamal Z. Zamli, and Rajan Jose. "Study of Keyword Extraction Techniques for Electric Double-Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis." Complexity 2021 (December 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/8192320.

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Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different su
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Wu, Chunguo, Maurizio Marchese, Jingqing Jiang, Alexander Ivanyukovich, and Yanchun Liang. "Machine Learning-Based Keywords Extraction for Scientific Literature." JUCS - Journal of Universal Computer Science 13, no. (10) (2007): 1471–83. https://doi.org/10.3217/jucs-013-10-1471.

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With the currently growing interest in the Semantic Web, keywords/metadata extraction is coming to play an increasingly important role. Keywords extraction from documents is a complex task in natural languages processing. Ideally this task concerns sophisticated semantic analysis. However, the complexity of the problem makes current semantic analysis techniques insufficient. Machine learning methods can support the initial phases of keywords extraction and can thus improve the input to further semantic analysis phases. In this paper we propose a machine learning-based keywords extraction for g
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Wang, Zhijuan, and Yinghui Feng. "FN-Rank: Domain Keywords Extraction Algorithm." Open Automation and Control Systems Journal 7, no. 1 (2015): 1347–51. http://dx.doi.org/10.2174/1874444301507011347.

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Yang, Li Gong, Jian Zhu, and Shi Ping Tang. "Keywords Extraction Based on Text Classification." Advanced Materials Research 765-767 (September 2013): 1604–9. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1604.

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In this paper, we propose new keywords extraction method based on texts classification. We first classify texts to determine their categories. Then determine weights of candidate words according to both their frequency and the relevance between text words and text category. Finally, keywords are extracted by sorting weights of candidate words. We conduct this experiment to show that on the premise of accurate text classification, this method can extract keywords effectively from text without title or with deviated title which can not reflect texts subject. Objective selecting of candidate word
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Mtafya, Ambele Robert, Dongjun Huang, and Gaudence Uwamahoro. "On Objective Keywords Extraction: Tf-Idf based Forward Words Pruning Algorithm for Keywords Extraction on YouTube." International Journal of Multimedia and Ubiquitous Engineering 9, no. 12 (2014): 97–106. http://dx.doi.org/10.14257/ijmue.2014.9.12.09.

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Dissertations / Theses on the topic "Keywords extraction"

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Stiff, Philip. "Analysis of Remarks Using Clustering and Keyword Extraction : Clustering Remarks on Electrical Installations and Identifying the Clusters by Extracting Keywords." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233200.

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Nowadays it is common for companies to sit on and gather a lot of data related to their business. The size of this data is often too large to be analyzed by hand and it is therefore becoming more and more common to automate this analysis e.g. by running machine learning methods on this data. In this project we attempt at analyzing an unstructured dataset consisting of remarks, found by inspectors, on electrical installations. This is done by firstly clustering the dataset with the goal of having each cluster representing a specific type of error found in the field and then extracting ten keywo
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Csomai, Andras. "Keywords in the mist: Automated keyword extraction for very large documents and back of the book indexing." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc6118/.

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This research addresses the problem of automatic keyphrase extraction from large documents and back of the book indexing. The potential benefits of automating this process are far reaching, from improving information retrieval in digital libraries, to saving countless man-hours by helping professional indexers creating back of the book indexes. The dissertation introduces a new methodology to evaluate automated systems, which allows for a detailed, comparative analysis of several techniques for keyphrase extraction. We introduce and evaluate both supervised and unsupervised techniques, design
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Csomai, Andras Mihalcea Rada F. "Keywords in the mist automated keyword extraction for very large documents and back of the book indexing /." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-6118.

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Grosz, Sandra. "Keyword Extraction from Swedish Court Documents." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272117.

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This thesis addresses the problem of extracting keywords which represent the rulings and and grounds for the rulings in Swedish court documents. The problem of identifying the candidate keywords was divided into two steps; first preprocessing the documents and second extracting keywords using a keyword extraction algorithm on the preprocessed documents. The preprocessing methods used in conjunction with the keywords extraction algorithms were that of using stop words and a stemmer. Then, three different approaches for extracting keywords were used; one statistic approach, one machine learning
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Beleckis, Pavelas. "Raktažodžių atpažinimas lietuvių kalbos tekste." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110615_131741-11523.

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Senas posakis teigia – kas valdo informaciją – tas valdo pasaulį. Šis posakis puikiai tinka kalbant apie verslą. Verslo sėkmė priklauso nuo gebėjimo laiku gauti reikiamą informaciją, greitai ją apdoroti ir priimti tinkamus sprendimus tikslų įgyvendinimui. Tačiau su kiekvienais metais vis sparčiau augant informacijos srautams, tai atlikti tampa vis sudėtingiau. Bet šią užduotį gali palengvinti raktažodžių ir raktinių frazių identifikavimo metodų taikymas informacijos analizės metu. Magistro darbo tema – raktažodžių atpažinimas lietuvių kalbos tekste. Pagrindinius darbe paliestus klausimus galim
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Uchida, Yuki, Tomohiro Yoshikawa, Takeshi Furuhashi, Eiji Hirao, and Hiroto Iguchi. "Extraction of important keywords in free text of questionnaire data and visualization of relationship among sentences." IEEE, 2009. http://hdl.handle.net/2237/13897.

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Basinger, John A. "Detail Extraction from Electron Backscatter Diffraction Patterns." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2689.

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Cross-correlation based analysis of electron backscatter diffraction (EBSD) patterns and the use of simulated reference patterns has opened up entirely new avenues of insight into local lattice properties within EBSD scans. The benefits of accessing new levels of orientation resolution and multiple types of previously inaccessible data measures are accompanied with new challenges in characterizing microscope geometry and other error previously ignored in EBSD systems. The foremost of these challenges, when using simulated patterns in high resolution EBSD (HR-EBSD), is the determination of patt
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Kyjovský, Marek. "Extrakce klíčových slov z vědeckých článků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237148.

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The main goal of this thesis is to explore basic methods which is using for extraction of important words from articles. After that try to understand character of using keywords from the available set of testing English articles. Based on these findings, try to design and to implement a system which is using this methods. Then created system testing on the real English articles and after that try to analyse results.
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Sari, Pelin. "Preliminary Design And Construction Of A Prototype Canola Seed Oil Extraction Machine." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607357/index.pdf.

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Growing energy demand in the world force people to investigate alternative energy sources. Unlike coal or other fossil fuels, renewable energy sources are promising for the future. Especially, seed oils are effectively used as energy sources such as fuel for diesel engines. The scope of this study is to develop an oil extraction machine specific to canola seed. In this study, seed oil extraction methods have been investigated and various alternatives for the extraction machine have been considered. For continuous operation, oil extraction with a screw press is evaluated as the most appropriate
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Matička, Jiří. "Extrakce klíčových slov z dokumentů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236533.

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This thesis pursues an automated extraction of keywords from documents. Its goal is to design and implement an application which will be able to extract an appropriate set of keywords related to the contents of the document. The major requirements for the application are speed and accuracy. That is why the first part of the thesis talks about already developed principles and a detailed classification based on various criteria. The second part is focused on choosing and a thorough functional describing of one of the methods which should have been used for extracting the keywords. The next parts
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Books on the topic "Keywords extraction"

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Dawn, Archer, ed. What's in a word-list?: Investigating word frequency and keyword extraction. Ashgate Pub. Ltd., 2008.

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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Povinelli, Elizabeth A. Between Gaia and Ground. Duke University Press, 2021. http://dx.doi.org/10.1215/9781478021872.

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In Between Gaia and Ground Elizabeth A. Povinelli theorizes the climatic, environmental, viral, and social catastrophe present as an ancestral catastrophe through which that Indigenous and colonized peoples have been suffering for centuries. In this way, the violence and philosophies the West relies on now threaten the West itself. Engaging with the work of Glissant, Deleuze and Guattari, Césaire, and Arendt, Povinelli highlights four axioms of existence—the entanglement of existence, the unequal distribution of power, the collapse of the event as essential to political thought, and the legaci
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Archer, Dawn. What's in a Word-List?: Investigating Word Frequency and Keyword Extraction. Taylor & Francis Group, 2016.

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Archer, Dawn. What's in a Word-List?: Investigating Word Frequency and Keyword Extraction. Taylor & Francis Group, 2016.

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Archer, Dawn. What's in a Word-List?: Investigating Word Frequency and Keyword Extraction. Taylor & Francis Group, 2016.

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Paul, Dimple Valayil. Developing a Keyword Extractor and Document Classifier: Emerging Research and Opportunities. IGI Global, 2021.

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Paul, Dimple Valayil. Developing a Keyword Extractor and Document Classifier: Emerging Research and Opportunities. IGI Global, 2021.

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Paul, Dimple Valayil. Developing a Keyword Extractor and Document Classifier: Emerging Research and Opportunities. IGI Global, 2021.

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Paul, Dimple Valayil. Developing a Keyword Extractor and Document Classifier: Emerging Research and Opportunities. IGI Global, 2021.

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Book chapters on the topic "Keywords extraction"

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Moreno-Ortiz, Antonio. "Keywords." In Making Sense of Large Social Media Corpora. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52719-7_4.

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AbstractThis chapter tackles the task of keyword extraction from corpora. Keywords are extremely helpful to quickly identify the terms (and their associated concepts) that somehow define what a corpus is about. After a quick revision of the concept of keyword, I focus on the different methods that have been proposed to extract keywords effectively and efficiently. A key distinction is made between the reference-corpus method traditionally employed in corpus linguistics and the various methods that have been proposed in Natural Language Processing research. Through several experiments, the CCTC
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Colla, Davide, Enrico Mensa, and Daniele P. Radicioni. "Semantic Measures for Keywords Extraction." In AI*IA 2017 Advances in Artificial Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70169-1_10.

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Gupta, Vishal. "A New Punjabi Keywords Extraction System." In Lecture Notes in Electrical Engineering. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1817-3_46.

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HaCohen-Kerner, Yaakov. "Automatic Extraction of Keywords from Abstracts." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_112.

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Wei, Yang. "An Iterative Approach to Keywords Extraction." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31020-1_12.

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Liu, Jianyi, Cong Wang, Zhengyang Liu, and Wenbin Yao. "Advertising Keywords Extraction from Web Pages." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16515-3_42.

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Singh, Harkirat, Mukesh Kumar, and Preeti Aggarwal. "Extraction and Sequencing of Keywords from Twitter." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7563-6_19.

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Palomino, Marco A., and Tom Wuytack. "Unsupervised Extraction of Keywords from News Archives." In Human Language Technology. Challenges for Computer Science and Linguistics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20095-3_50.

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Lei, Kai, Hanhong Tang, and YiFan Zeng. "Keywords Extraction via Multi-relational Network Construction." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00951-3_4.

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Gupta, Vishal. "A New Hybrid Language Independent Keywords Extraction System." In Lecture Notes in Electrical Engineering. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1817-3_44.

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Conference papers on the topic "Keywords extraction"

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Das, Priyanka, Anuvab Munshi, Annwesha Naha, Adrija Dey, and Sriparno Chakraborty. "Graph Based Keywords Extraction from Crime Data." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847425.

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Das, Priyanka, Anuvab Munshi, Annwesha Naha, Adrija Dey, and Sriparno Chakraborty. "Graph Based Keywords Extraction from Crime Data." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847496.

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Luo, Yanfen. "Keywords extraction algorithm based on attention mechanism of BERT model." In International Conference on Optics, Electronics, and Communication Engineering, edited by Yang Yue. SPIE, 2024. http://dx.doi.org/10.1117/12.3049066.

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Rinartha, Komang, and Luh Gede Surya Kartika. "Rapid Automatic Keyword Extraction and Word Frequency in Scientific Article Keywords Extraction." In 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2021. http://dx.doi.org/10.1109/icoris52787.2021.9649458.

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Shihua Xu and Fang Kong. "Toward better keywords extraction." In 2015 International Conference on Asian Language Processing (IALP). IEEE, 2015. http://dx.doi.org/10.1109/ialp.2015.7451561.

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Suleiman, Dima, Arafat A. Awajan, and Wael al Etaiwi. "Arabic Text Keywords Extraction using Word2vec." In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS). IEEE, 2019. http://dx.doi.org/10.1109/ictcs.2019.8923034.

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Pan, Suhan, Zhiqiang Li, and Juan Dai. "An improved TextRank keywords extraction algorithm." In ACM TURC 2019: ACM Turing Celebration Conference - China. ACM, 2019. http://dx.doi.org/10.1145/3321408.3326659.

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Yang, Li-gong, Jian Zhu, and Shi-ping Tang. "Keywords Extraction Based on Text Classification." In 2nd International Conference On Systems Engineering and Modeling. Atlantis Press, 2013. http://dx.doi.org/10.2991/icsem.2013.150.

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Chen, Chaoxian, Bo Yang, and Changjian Zhao. "Keywords Extraction Based on Word Relevance Degrees." In ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence. ACM, 2020. http://dx.doi.org/10.1145/3395260.3395262.

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Zhang, Yong, Fen Chen, Wufeng Zhang, Haoyang Zuo, and Fangyuan Yu. "Keywords Extraction Based on Word2Vec and TextRank." In ICBDE '20: 2020 The 3rd International Conference on Big Data and Education. ACM, 2020. http://dx.doi.org/10.1145/3396452.3396460.

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Reports on the topic "Keywords extraction"

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AlBakri, Aref, Auswaf Ahsan, Manoj Vengal, KR Ashir, Abdul Majeed, and Hanan Siddiq. Antibiotic Prophylaxis before Invasive Dental Procedures for Patients at High-Risk of Infective Endocarditis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.7.0011.

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Review question / Objective: The aim of the present systematic review and meta-analysis is to determine whether there is a genuine clinical need for Antibiotic Prophylaxis(AP) for the prevention of Infective Endocarditis(IE) in high-risk individuals (particularly those with demonstrable structural heart diseases or valve surgery) undergoing invasive dental procedures. Information sources: PubMed, Science Direct, British Dental Journal and Cochrane Register of Controlled Trials. Search terms used included various combinations of the following subject headings and title or abstract keywords – pr
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