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Journal articles on the topic 'Keywords extraction'

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

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1

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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Chen, Yi-Hui, Eric Jui-Lin Lu, and Meng Fang Tsai. "Finding keywords in blogs: Efficient keyword extraction in blog mining via user behaviors." Expert Systems with Applications 41, no. 2 (2014): 663–70. http://dx.doi.org/10.1016/j.eswa.2013.07.091.

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Liu, Feng, Xiaodi Huang, Weidong Huang, and Sophia Xiaoxia Duan. "Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments." Symmetry 12, no. 11 (2020): 1923. http://dx.doi.org/10.3390/sym12111923.

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Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comme
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Chen, Sheng, Weijun Pan, Yidi Wang, Shenhao Chen, and Xuan Wang. "Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model." Aerospace 12, no. 5 (2025): 376. https://doi.org/10.3390/aerospace12050376.

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In recent years, with the increasing complexity of air traffic management and the rapid development of automation technology, efficiently and accurately extracting key information from large volumes of air traffic control (ATC) instructions has become essential for ensuring flight safety and improving the efficiency of air traffic control. However, this task is challenging due to the specialized terminology involved and the high real-time requirements for data collection and processing. While existing keyword extraction methods have made some progress, most of them still perform unsatisfactori
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Strube, Tom, Tom Nowak, Mariia Pokotylo, and Bernd Kuhlenkötter. "Reliable and Content-specific Support for Keyword Selection through AI and Statistics." Current Directions in Biomedical Engineering 10, no. 4 (2024): 627–30. https://doi.org/10.1515/cdbme-2024-2154.

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Abstract Due to the recent popularity and availability of Large Language Models (LLMs), creators of educational materials can more efficiently extract keywords for use in personalised learning recommendations than ever before. However, due to the LLMs’ probabalistic nature, the automation of the otherwise labour-intense keyword extraction inherits the risk of biased and non-explainable results. In this research, we present an original framework to enhance keyword selection based on content title and description through a novel, reliability-sensitive, keyword selection algorithm. For this, we c
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Zhang, Kun. "Web News Data Extraction Technology Based on Text Keywords." Complexity 2021 (April 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/5529447.

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In order to shorten the time for users to query news on the Internet, this paper studies and designs a network news data extraction technology, which can obtain the main news information through the extraction of news text keywords. Firstly, the TF-IDF keyword extraction algorithm, TextRank keyword extraction algorithm, and LDA keyword extraction algorithm are analyzed to understand the keyword extraction process, and the TF-IDF algorithm is optimized by Zipf’s law. By introducing the idea of model fusion, five schemes based on waterfall fusion and parallel combination fusion are designed, and
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Alrasheed, Hend. "Word synonym relationships for text analysis: A graph-based approach." PLOS ONE 16, no. 7 (2021): e0255127. http://dx.doi.org/10.1371/journal.pone.0255127.

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Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywor
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Mehrabi, Elahe, Azadeh Mohebi, and Abbas Ahmadi. "Improving the RAKE Algorithm for Keyword Extraction from Persian Scientific Texts: A Case Study of Persian Theses and Dissertations." Journal of Information Processing and Management 37, no. 1 (2021): 197–228. https://doi.org/10.5281/zenodo.14035093.

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Keywords are subsets of words or phrases within a document that can describe the meaning of the document and play a crucial role in the information retrieval process. Since the extraction of keywords or key phrases from specialized and scientific texts is a specialized and time-consuming task, and the volume of scientific documents requiring keywords is increasing, various algorithms have been designed and implemented for the specialized and automatic extraction of keywords and key phrases from documents.  RAKE (Rapid Automatic Keyword Extraction) is a widely used algorithm for extracting
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Bashmakova, Anastasiia Yurievna. "Keywords Extraction on the Topic "Образование/Education"". Filologičeskie nauki. Voprosy teorii i praktiki, № 12 (грудень 2021): 3986–90. http://dx.doi.org/10.30853/phil20210598.

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Zhang, Yu, Mingxiang Tuo, Qingyu Yin, Le Qi, Xuxiang Wang, and Ting Liu. "Keywords extraction with deep neural network model." Neurocomputing 383 (March 2020): 113–21. http://dx.doi.org/10.1016/j.neucom.2019.11.083.

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Hassan, Rohayanti, Noor Atikah Amira Fauzi, Shahreen Kasim, and Herman Khalid Omer. "Usability Requirements Extraction Method from Software Document." International Journal of Software Engineering and Knowledge Engineering 30, no. 02 (2020): 171–89. http://dx.doi.org/10.1142/s0218194020500084.

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Extracting the usability requirements are crucial during requirements review and requirements validation for different purposes. Usability requirements are hard to be determined until the real user has experienced the software. It is even more challenging when these usability requirements are documented in natural language, which has an inconsistent and unrestricted structure. Automated requirements extraction has been widely studied to facilitate the process of requirements checking. Nevertheless, the accuracy of requirements extraction method still can be improved. Thus, this paper has prese
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Yang, Chang, Xing Hua Li, and Xue Guang Zhou. "Tacit Extraction for Keyword in Chinese." Applied Mechanics and Materials 58-60 (June 2011): 1415–20. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1415.

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The present Extraction for Keyword in Chinese (EKC for short) is merely directed against explicit keywords or prototype keywords, and has not take into account those tacit keywords distorted by network hackers with the method of active jamming in Chinese. For this purpose, with the help of M. Polanyi’s theory of tacit knowledge, this paper presents a new approach for the tacit EKC (TEKC for short), which can improve the ratio of precision and recall for information filtering. Based on the TEKC, the paper presents a set of classifications of how to distort the explicit keywords and the solution
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Hashemzahde, Bahare, and Majid Abdolrazzagh-Nezhad. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5909. http://dx.doi.org/10.11591/ijece.v10i6.pp5909-5916.

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to t
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Bahareh, Hashemzadeh, and Abdolrazzagh-Nezhad Majid. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5909–16. https://doi.org/10.11591/ijece.v10i6.pp5909-5916.

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to t
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Li, Jinye. "A comparative study of keyword extraction algorithms for English texts." Journal of Intelligent Systems 30, no. 1 (2021): 808–15. http://dx.doi.org/10.1515/jisys-2021-0040.

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Abstract This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–ID
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Zhao, Hong, Chen Sheng Bai, and Song Zhu. "Automatic Keyword Extraction Algorithm and Implementation." Applied Mechanics and Materials 44-47 (December 2010): 4041–49. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.4041.

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Search engines can bring a lot of benefit to the website. For a site, each page’s search engine ranking is very important. To make web page ranking in search engine ahead, Search engine optimization (SEO) make effect on the ranking. Web page needs to set the keywords as “keywords" to use SEO. The paper focuses on the content of a given word, and extracts the keywords of each page by calculating the word frequency. The algorithm is implemented by C # language. Keywords setting of webpage are of great importance on the information and products
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Yoo, Chongjae, and Hwanhee Lee. "Improving Abstractive Dialogue Summarization Using Keyword Extraction." Applied Sciences 13, no. 17 (2023): 9771. http://dx.doi.org/10.3390/app13179771.

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Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first
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Huang, Qikang. "Research on Keywords Extraction of Film Reviews Based on the KeyBERT Model." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 732–38. http://dx.doi.org/10.62051/1zpndy68.

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Current film and television platforms still struggle to effectively gather information for users and companies through public film reviews. There is a lack of applications or research on keyword extraction from film reviews. This study aims to evaluate the effectiveness and feasibility of the KeyBERT model for extracting keywords from film reviews. The precision and recall rate are utilized to assess the impact of model extraction. The test results indicate that the average precision and recall rate of film review extraction are 0.600 and 0.387, respectively, which are slightly lower than thos
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LAHIRI, S., R. MIHALCEA, and P. H. LAI. "Keyword extraction from emails." Natural Language Engineering 23, no. 2 (2016): 295–317. http://dx.doi.org/10.1017/s1351324916000231.

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AbstractEmails constitute an important genre of online communication. Many of us are often faced with the daunting task of sifting through increasingly large amounts of emails on a daily basis. Keywords extracted from emails can help us combat such information overload by allowing a systematic exploration of the topics contained in emails. Existing literature on keyword extraction has not covered the email genre, and no human-annotated gold standard datasets are currently available. In this paper, we introduce a new dataset for keyword extraction from emails, and evaluate supervised and unsupe
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Cruz, Fred Torres, Edelfré Flores Velásquez, William Eusebio Arcaya Coaquira, Irenio Luis Chagua Aduviri, and Marga Isabel Ingaluque Arapa. "Unsupervised Learning Algorithms for Keyword Extraction in an Undergraduate Thesis." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 785–90. http://dx.doi.org/10.22214/ijraset.2022.40758.

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Abstract: The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communication technologies, such as the platform for integrated research and academic work with responsibility (PILAR), which records information about research projects, such as titles, summaries, and keywords in their various modalities, have gained relevance and importance
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Peng, Hao, and Zhen Chen. "Visual KEA: A Visual Model Based on Keywords Extraction Algorithm for Hub Pages." Advanced Materials Research 532-533 (June 2012): 1593–99. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1593.

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Automatically extracting keywords from webpage is greatly important for focused spider. There are already quite many researches on automatically extracting keywords from content-intensive web pages. However, it is still a challenge to extract keywords automatically from hyperlink-intensive web pages (hub pages). The web page author will often use all kinds of visual strengthening means to prominently demonstrate some glossaries connected with the subject. Therefore, this paper proposes a visual model of web pages, DOM-PIXEL, which regards DOM leaf node of webpage as an image element expressed
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Chen, Liang-Ching. "An Improved Corpus-Based NLP Method for Facilitating Keyword Extraction: An Example of the COVID-19 Vaccine Hesitancy Corpus." Sustainability 15, no. 4 (2023): 3402. http://dx.doi.org/10.3390/su15043402.

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In the current COVID-19 post-pandemic era, COVID-19 vaccine hesitancy is hindering the herd immunity generated by widespread vaccination. It is critical to identify the factors that may cause COVID-19 vaccine hesitancy, enabling the relevant authorities to propose appropriate interventions for mitigating such a phenomenon. Keyword extraction, a sub-field of natural language processing (NLP) applications, plays a vital role in modern medical informatics. When traditional corpus-based NLP methods are used to conduct keyword extraction, they only consider a word’s log-likelihood value to determin
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Ayub, Nafees, Muhammad Ramzan Talib, Muhammad Kashif Hanif, and Muhammad Awais. "Aspect Extraction Approach for Sentiment Analysis Using Keywords." Computers, Materials & Continua 74, no. 3 (2023): 6879–92. http://dx.doi.org/10.32604/cmc.2023.034214.

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Meng, Fan, Kaile Zhou, Yi Bu, et al. "Keywords Extraction and Thesaurus Construction for Domain News." Procedia Computer Science 214 (2022): 837–44. http://dx.doi.org/10.1016/j.procs.2022.11.249.

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kadhem, S., and A. Almeer. "Arabic Texts Classification Based on Keywords Extraction Technique." Engineering and Technology Journal 35, no. 2B (2017): 96–104. http://dx.doi.org/10.30684/etj.2017.138549.

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Takase, Haruhiko, Hiroharu Kawanaka, and Shinji Tsuruoka. "Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface –." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 1 (2015): 152–57. http://dx.doi.org/10.20965/jaciii.2015.p0152.

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We focus on developing an e-learning system that supports the grasping of misunderstanding from descriptive answers. We propose real-time keyword extraction and an interface for grasping misunderstanding based on extracted keywords. The system extracts keywords without extra information. Teachers find majormisunderstandings by using the proposed interface, which consists of two views – keyword and description. Using these views, teachers browse answers in three steps – finding keywords, reading around keywords, and reading full answers. We use experiments to demonstrate the effectiveness of ou
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Logaiyan, P., R. Ramakrishnan, V. Deepa, and K. Narmatha. "AI-Powered Keyword Extraction System Using NLP Techniques for Contextual Insights and Document Accessibility." International Scientific Journal of Engineering and Management 04, no. 01 (2025): 1–7. https://doi.org/10.55041/isjem02210.

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This study presents the development of an AI-powered keyword extraction system using Python, aimed at efficiently identifying significant keywords from unstructured text data. Leveraging advanced natural language processing (NLP) techniques, the system integrates multiple methodologies, including Term Frequency-Inverse Document Frequency (TF-IDF), TextRank, Latent Semantic Analysis (LSA), and Part-of-Speech (POS) tagging. The incorporation of POS tagging enhances the accuracy of keyword identification by focusing on essential parts of speech, such as nouns and verbs, thereby filtering out irre
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Rammal, Mahmoud, Zeinab Bahsoun, and Mona Al Achkar Jabbour. "Keyword extraction from Arabic legal texts." Interactive Technology and Smart Education 12, no. 1 (2015): 62–71. http://dx.doi.org/10.1108/itse-11-2013-0030.

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Purpose – The purpose of this paper is to apply local grammar (LG) to develop an indexing system which automatically extracts keywords from titles of Lebanese official journals. Design/methodology/approach – To build LG for our system, the first word that plays the determinant role in understanding the meaning of a title is analyzed and grouped as the initial state. These steps are repeated recursively for the whole words. As a new title is introduced, the first word determines which LG should be applied to suggest or generate further potential keywords based on a set of features calculated fo
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I, Nyoman Prayana Trisna, and Nurwidyantoro Arif. "Single document keywords extraction in Bahasa Indonesia using phrase chunking." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1917–25. https://doi.org/10.12928/TELKOMNIKA.v18i4.14389.

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Keywords help readers to understand the idea of a document quickly. Unfortunately, considerable time and effort are often needed to come up with a good set of keywords manually. This research focused on generating keywords from a document automatically using phrase chunking. Firstly, we collected part of speech patterns from a collection of documents. Secondly, we used those patterns to extract candidate keywords from the abstract and the content of a document. Finally, keywords are selected from the candidates based on the number of words in the keyword phrases and some scenarios involving ca
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Pan, Teng, Bing Liu, Yan Shuang Liu, and Yun Jie Li. "Design and Implementation of Keywords Extraction and Management System Based on Java Platform." Advanced Materials Research 765-767 (September 2013): 1596–99. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1596.

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This essay discusses the design and implementation of a keywords extraction and management system based on java platform. By using development methodology of domain engineering, the essay focuses on key technology of keywords extraction system and outline some ideas of the software architecture, in which identify basic principles and requirements of the development of keywords extraction and management system. The system functions are implemented with Java language in Eclipse environment. The system is featured with expandability, reliability, suitability and friendly interface demonstrated in
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Zhao, Hongyang, and Qiang Xie. "An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources." Journal of Physics: Conference Series 2078, no. 1 (2021): 012021. http://dx.doi.org/10.1088/1742-6596/2078/1/012021.

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Abstract In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the
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Nadamoto, Akiyo, and Keigo Sakai. "Detecting and presenting welcome-news for tourists from user reviews." International Journal of Web Information Systems 13, no. 4 (2017): 354–69. http://dx.doi.org/10.1108/ijwis-04-2017-0033.

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Purpose Recently, people usually use the internet to obtain travel information, when they plan their travel. They especially want to obtain sightseeing spot information from reviews, but there are huge amounts of reviews of sightseeing spots. Users therefore cannot obtain important information from the reviews easily. As described herein, this paper aims to propose a system that automatically extracts and presents welcome news for sightseeing spots from reviews. This proposed Welcome-news is a “useful information” and “unexpected information” related to travel. Design/methodology/approach The
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Rodzuan, Nur Aniq Syafiq, Shahreen Kasim, Mohanavali Sithambranathan, and Muhammad Zaki Hassan. "Classification of Biomedical Literature in Hypertension and Diabetes." International Journal on Data Science 1, no. 2 (2020): 114–19. http://dx.doi.org/10.18517/ijods.1.2.114-119.2020.

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Textual information gives us more clear information as it is presented using words and characters, which is easy for humans to understand. To extract this kind of information, text mining was introduced as new technology. Text mining is the process of extracting non-trivial patterns or knowledge from text documents or from textual databases. The purpose of this research paper is to perform and compare keyword extraction using statistical and linguistic extraction tools for 120 text documents related to hypertension and diabetes disease. In order to draw this comparison, RStudio, a statistical-
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Ren, Wei, Hengwei Zhang, and Ming Chen. "A Method of Domain Dictionary Construction for Electric Vehicles Disassembly." Entropy 24, no. 3 (2022): 363. http://dx.doi.org/10.3390/e24030363.

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Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and
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Bertels, Ann, and Dirk Speelman. "‘Keywords Method’ versus ‘Calcul des Spécificités’." International Journal of Corpus Linguistics 18, no. 4 (2013): 536–60. http://dx.doi.org/10.1075/ijcl.18.4.04ber.

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This paper explores two tools and methods for keyword extraction. As several tools are available, it makes a comparison of two widely used tools, namely Lexico3 (Lamalle et al. 2003) and WordSmith Tools (Scott 2013). It shows the importance of keywords and discusses recent studies involving keyword extraction. Since no previous study has attempted to compare two different tools, used by different language communities and which use different methodologies to extract keywords, this paper aims at filling the gap by comparing not only the tools and their practical use, but also the underlying meth
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Beliga, Slobodan, Ana Meštrović, and Sanda Martinčić-Ipšić. "Selectivity-Based Keyword Extraction Method." International Journal on Semantic Web and Information Systems 12, no. 3 (2016): 1–26. http://dx.doi.org/10.4018/ijswis.2016070101.

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In this work the authors propose a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The authors show that selectivity-based keyword extraction slightly outperforms an extraction based on the standard centrality measures: in/out-degree, betweenness and closeness. Therefore, they include selectivity and its modificati
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Ni, Pin, Yuming Li, and Victor Chang. "Research on Text Classification Based on Automatically Extracted Keywords." International Journal of Enterprise Information Systems 16, no. 4 (2020): 1–16. http://dx.doi.org/10.4018/ijeis.2020100101.

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Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize tex
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Khan, Osama A., Shaukat Wasi, Muhammad Shoaib Siddiqui, and Asim Karim. "Keyword Extraction for Medium-Sized Documents Using Corpus-Based Contextual Semantic Smoothing." Complexity 2022 (September 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/7015764.

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Keyword extraction refers to the process of selecting most significant, relevant, and descriptive terms as keywords, which are present inside a single document. Keyword extraction has major applications in the information retrieval domain, such as analysis, summarization, indexing, and search, of documents. In this paper, we present a novel supervised technique for extraction of keywords from medium-sized documents, namely Corpus-based Contextual Semantic Smoothing (CCSS). CCSS extends the concept of Contextual Semantic Smoothing (CSS), which considers term usage patterns in similar texts to i
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Khan, Muhammad Qasim, Abdul Shahid, M. Irfan Uddin, et al. "Impact analysis of keyword extraction using contextual word embedding." PeerJ Computer Science 8 (May 30, 2022): e967. http://dx.doi.org/10.7717/peerj-cs.967.

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A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the seman
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Guan, Xinxin, Yeli Li, and Hechen Gong. "Improved TF-IDF for We Media Article Keywords Extraction." Journal of Physics: Conference Series 1302 (August 2019): 032003. http://dx.doi.org/10.1088/1742-6596/1302/3/032003.

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Wang, Zihuan, Kyusup Hahn, Youngsam Kim, Sanghyup Song, and Jong-Mo Seo. "A news-topic recommender system based on keywords extraction." Multimedia Tools and Applications 77, no. 4 (2017): 4339–53. http://dx.doi.org/10.1007/s11042-017-5513-0.

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