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Liu, Hanwen, Huaizhen Kou, Chao Yan, and Lianyong Qi. "Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph." Complexity 2020 (April 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/2085638.
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Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.
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Isenberg, Petra, Tobias Isenberg, Michael Sedlmair, Jian Chen, and Torsten Moller. "Visualization as Seen through its Research Paper Keywords." IEEE Transactions on Visualization and Computer Graphics 23, no. 1 (2017): 771–80. http://dx.doi.org/10.1109/tvcg.2016.2598827.
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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 reviews on the Internet. This paper proposes a method for extracting evaluation keywords and displays its results as graphs. It employs HK Graph (Hierarchical Keyword Graph), which can visualize the relationship among words in a hierarchical network structure based on the co-occurrence information for the keyword graph.
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Webb, Christine. "Choosing keywords for JAN papers." Journal of Advanced Nursing 51, no. 3 (2005): 203. http://dx.doi.org/10.1111/j.1365-2648.2005.03485.x.
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Culpeper, Jonathan. "Keyness." International Journal of Corpus Linguistics 14, no. 1 (2009): 29–59. http://dx.doi.org/10.1075/ijcl.14.1.03cul.
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This paper explores keywords, key part-of-speech categories and key semantic categories and their role in text analysis. The first part of the paper addresses a set of issues relating to the definition of keywords and their history, the settings used in deriving keywords, the choice of reference corpora, the different kinds of keyword that emerge in one’s results and the dispersion of keywords in one’s data. It argues, amongst other things, that keywords are the same as style markers, and that three types of keyword can be identified: interpersonal, textual and ideational. The second part of the paper addresses the question of what precisely is to be gained from analysing key part-of-speech or key semantic domains in addition to keywords. It shows that whilst in general they add little to a keyword analysis, which is in any case methodologically more robust, there are some significant specific benefits. Answers to many of the questions posed in this paper are illustrated by a study of character-talk from Shakespeare’s play Romeo and Juliet, and in this way this paper also makes a contribution to the fledging field of corpus stylistics.
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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 methodologies and statistics. By means of a comparative study on a small test corpus, this paper shows major similarities and differences between the tools. The similarities mainly concern the most typical keywords, whereas the differences concern the total number of significant keywords extracted, the granularity of both probability value and typicality coefficient and the type of the reference corpus.
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Utiyama, Masao, Masaki Murata, and Hitoshi Isahara. "Using author keywords for automatic term recognition." Terminology 6, no. 2 (2000): 313–26. http://dx.doi.org/10.1075/term.6.2.10uti.
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This paper proposes a method which regards the keywords provided by the authors of technical papers as terms and learns the statistics which distinguish terms from non-terms. Since it uses keywords as training data, it requires no hand-labeled training corpora manually annotated with terms. The proposed method was used to extract terms from the NTCIR morphologically tagged corpus and achieved 0.800 recall and 0.431 precision. The effectiveness of the proposed method has thus been demonstrated.
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Huang, Yue, Hu Liu, and Jing Pan. "Identification of data mining research frontier based on conference papers." International Journal of Crowd Science 5, no. 2 (2021): 143–53. http://dx.doi.org/10.1108/ijcs-01-2021-0001.
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Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
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Kalmukov, Yordan. "Describing papers and reviewers’ competences by taxonomy of keywords." Computer Science and Information Systems 9, no. 2 (2012): 763–89. http://dx.doi.org/10.2298/csis110906012k.
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This article focuses on the importance of the precise calculation of similarity factors between papers and reviewers for performing a fair and accurate automatic assignment of reviewers to papers. It suggests that papers and reviewers? competences should be described by taxonomy of keywords so that the implied hierarchical structure allows similarity measures to take into account not only the number of exactly matching keywords, but in case of non-matching ones to calculate how semantically close they are. The paper also suggests a similarity measure derived from the well-known and widely-used Dice's coefficient, but adapted in a way it could be also applied between sets whose elements are semantically related to each other (as concepts in taxonomy are). It allows a non-zero similarity factor to be accurately calculated between a paper and a reviewer even if they do not share any keyword in common.
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Gheni, Hadeel Qasem, Ahmed Mohammed Hussein, and Wed Kadhim Oleiwi. "Suggesting new words to extract keywords from title and abstract." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4441. http://dx.doi.org/10.11591/ijece.v9i5.pp4441-4445.
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When talking about the fundamentals of writing research papers, we find that keywords are still present in most research papers, but that does not mean that they exist in all of them, we can find papers that do not contain keywords. Keywords are those words or phrases that accurately reflect the content of the research paper. Keywords are an exact abbreviation of what the research carries in its content. The right keywords may increase the chance of finding the article or research paper and chances of reaching more people who should reach them. The importance of keywords and the essence of the research and address is mainly to attract these highly specialized and highly influential writers in their fields and who specialize in reading what holds the appropriate characteristics but they do not read and cannot read everything. In this paper, we extract new keywords by suggesting a set of words, these words were suggested according to the many mentioned in the researches with multiple disciplines in the field of computer. In our system, we take a number of words (as many as specified in the program) that come before the proposed words and consider it as new keywords. This system proved to be effective in finding keywords that correspond to some extent with the keywords developed by the author in his research.
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Gurtu, Amulya, Cory Searcy, and M. Y. Jaber. "An analysis of keywords used in the literature on green supply chain management." Management Research Review 38, no. 2 (2015): 166–94. http://dx.doi.org/10.1108/mrr-06-2013-0157.
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Purpose The purpose of this paper is to analyze the keywords used in peer-reviewed literature on green supply chain management. Design/methodology/approach To determine the keywords that were used in this area, an analysis of 629 papers was conducted. The papers were identified through searches of 13 keywords on green supply chains. Trends in keyword usage were analyzed in detail focusing on examining variables such as the most frequently used journals/keywords, their frequencies, citation frequency and research contribution from different disciplines/countries. Findings A number of different terms have been used for research focused on the environmental impacts of supply chains, including green supply chains, sustainable supply chains, reverse logistics and closed-loop supply chains, among others. The analysis revealed that the intensity of research in this area has more than tripled in the past six years and that the most used keyword was “reverse logistics”. The use of the terms “green supply chains” and “sustainable supply chains” is increasing, and the use of “reverse logistics” is decreasing. Research limitations/implications The analysis is limited to 629 papers from the Scopus database during the period of 2007 and 2012. Originality/value The paper presents the first systematic analysis of keywords used in the literature on green supply chains. Given the broad array of terms used to refer to research in this area, this is a needed contribution. This work will help researchers in choosing keywords with high frequency and targeting journals for publishing their future work. The paper may also provide a basis for further work on developing consolidated definitions of terms focused on green supply chain management.
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Ceric, Anita, and Josip Sertic. "The Engineering Project Organization Society and megaprojects: literature analysis using keywords." Organization, Technology and Management in Construction: an International Journal 11, no. 1 (2019): 1968–74. http://dx.doi.org/10.2478/otmcj-2018-0015.
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Abstract The purpose of this paper is to analyze how the Engineering Project Organization Society (EPOS) has addressed the issue of megaprojects at their annual conferences organized from 2006 to 2016. The literature analysis used in this paper is a form of content analysis. It focuses on the usage of a particular term in scientific papers. In this case, the key term is “megaprojects” or “mega-projects”. Papers in which this term appears are selected for further analysis. The findings show that the main keyword “megaproject” or “mega-project” appears 22 times in the identified papers. It appears in 10 titles and nine abstracts. Most important for this literature analysis, it appears in seven lists of keywords. Literature analysis proceeded by analyzing the associated keywords in the seven papers in which the main keyword “megaproject” or “mega-projects” can be found in the listed keywords. The analysis shows that the main associated keywords are “governance”, “complexity”, and “trust”. This research provides a view of the collective understanding of megaprojects within the EPOS community and helps to shape further research in this field. In addition, the results of this research can be seen as a step forward for scholars and practitioners to discuss and develop a new theoretical framework for better understanding of megaproject governance.
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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 that language holds a high rank based on the keywords criteria in other languages, as well. To achieve to this aim, the average TF-IDF of the candidate words were calculated for the same and the other languages. Then the words with the higher averages TF-IDF were chosen as the extracted keywords. The obtained results indicat that the algorithms’ accuracis of the multilingual texts in term frequency-inverse document frequency (TF-IDF) algorithm, graph-based algorithm, and the improved proposed algorithm are 80%, 60.65%, and 91.3%, respectively.
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Selvan, Mercy Paul, A. Viji Amutha Mary, and S. Jancy. "Automatic User Domain Classification Based on Support Vector Machine (SVM)." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3327–31. http://dx.doi.org/10.1166/jctn.2019.8187.
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Finding domain of a research paper and a researcher is a crucial task and would be highly appreciable in order to provide personalized search results to the user. An automatic user domain classification technique based on SVM has been proposed in this paper in order to determine the domain of a user based on her publications. In this technique, for a given user, his specific area of domain is determined by classifying the keywords from his publication works. It consists of two phases: keyword extraction and domain classification. In keyword extraction phase, the list of publications corresponding to a user mail id is retrieved by using publish or perish tool. From each of the published papers, the keywords are extracted. In domain classification, SVM classifier is applied to determine the domain of the user. This is performed by training standard keywords from each domain into the SVM classifier. If a user belongs to more than one domain, then the primary domain with more publications will be considered.
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Li, Haoran, Junnan Zhu, Jiajun Zhang, Chengqing Zong, and Xiaodong He. "Keywords-Guided Abstractive Sentence Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8196–203. http://dx.doi.org/10.1609/aaai.v34i05.6333.
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We study the problem of generating a summary for a given sentence. Existing researches on abstractive sentence summarization ignore that keywords in the input sentence provide significant clues for valuable content, and humans tend to write summaries covering these keywords. In this paper, we propose an abstractive sentence summarization method by applying guidance signals of keywords to both the encoder and the decoder in the sequence-to-sequence model. A multi-task learning framework is adopted to jointly learn to extract keywords and generate a summary for the input sentence. We apply keywords-guided selective encoding strategies to filter source information by investigating the interactions between the input sentence and the keywords. We extend pointer-generator network by a dual-attention and a dual-copy mechanism, which can integrate the semantics of the input sentence and the keywords, and copy words from both the input sentence and the keywords. We demonstrate that multi-task learning and keywords-oriented guidance facilitate sentence summarization task, achieving better performance than the competitive models on the English Gigaword sentence summarization dataset.
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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 weighting function still needs to be further researched.
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Wang, Hao, and Sanhong Deng. "A paper-text perspective." Electronic Library 35, no. 4 (2017): 689–708. http://dx.doi.org/10.1108/el-09-2016-0192.
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Purpose In the era of Big Data, network digital resources are growing rapidly, especially the short-text resources, such as tweets, comments, messages and so on, are showing a vigorous vitality. This study aims to compare the categories discriminative capacity (CDC) of Chinese language fragments with different granularities and to explore and verify feasibility, rationality and effectiveness of the low-granularity feature, such as Chinese characters in Chinese short-text classification (CSTC). Design/methodology/approach This study takes discipline classification of journal articles from CSSCI as a simulation environment. On the basis of sorting out the distribution rules of classification features with various granularities, including keywords, terms and characters, the classification effects accessed by the SVM algorithm are comprehensively compared and evaluated from three angles of using the same experiment samples, testing before and after feature optimization, and introducing external data. Findings The granularity of a classification feature has an important impact on CSTC. In general, the larger the granularity is, the better the classification result is, and vice versa. However, a low-granularity feature is also feasible, and its CDC could be improved by reasonable weight setting, even exceeding a high-granularity feature if synthetically considering classification precision, computational complexity and text coverage. Originality/value This is the first study to propose that Chinese characters are more suitable as descriptive features in CSTC than terms and keywords and to demonstrate that CDC of Chinese character features could be strengthened by mixing frequency and position as weight.
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Haunschild, Robin, and Werner Marx. "Discovering seminal works with marker papers." Scientometrics 125, no. 3 (2020): 2955–69. http://dx.doi.org/10.1007/s11192-020-03358-z.
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AbstractBibliometric information retrieval in databases can employ different strategies. Commonly, queries are performed by searching in title, abstract and/or author keywords (author vocabulary). More advanced queries employ database keywords to search in a controlled vocabulary. Queries based on search terms can be augmented with their citing papers if a research field cannot be curtailed by the search query alone. Here, we present another strategy to discover the most important papers of a research field. A marker paper is used to reveal the most important works for the relevant community. All papers co-cited with the marker paper are analyzed using reference publication year spectroscopy (RPYS). For demonstration of the marker paper approach, density functional theory is used as a research field. Comparisons between a prior RPYS on a publication set compiled using a keyword-based search in a controlled vocabulary and three different co-citation RPYS analyses show very similar results. Similarities and differences are discussed.
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Yoon, Hee-Young, and Il-Youp Kwak. "The Association Modeling on Keywords and Documents of Logistics Research using Paper Abstract data." Korean Academy Of International Commerce 34, no. 3 (2019): 147–66. http://dx.doi.org/10.18104/kaic.2019.34.3.147.
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Zhang, Yu, Wei He, and Yin Li. "Efficient Boolean Keywords Search over Encrypted Cloud Data in Public Key Setting." Mobile Information Systems 2020 (August 26, 2020): 1–15. http://dx.doi.org/10.1155/2020/2904861.
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Searchable public key encryption- (SPE-) supporting keyword search plays an important role in cloud computing for data confidentiality. The current SPE scheme mainly supports conjunctive or disjunctive keywords search which belongs to very basic query operations. In this paper, we propose an efficient and secure SPE scheme that supports Boolean keywords search, which is more advanced than the conjunctive and disjunctive keywords search. We first develop a keyword conversion method, which can change the index and Boolean keywords query into a group of vectors. Then, through applying a technique so-called dual pairing vector space to encrypt the obtained vectors, we propose a concrete scheme proven to be secure under chosen keyword attack. Finally, we put forward a detailed theoretical and experimental analysis to demonstrate the efficiency of our scheme.
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Sesagiri Raamkumar, Aravind, Schubert Foo, and Natalie Pang. "Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems." Information Processing & Management 53, no. 3 (2017): 577–94. http://dx.doi.org/10.1016/j.ipm.2016.12.006.
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Torres, Tiago, Ana Ferreira, Paulo Ferreira, et al. "Portuguese Position Paper on the Use of Biosimilars in Psoriasis." Acta Médica Portuguesa 29, no. 9 (2016): 574. http://dx.doi.org/10.20344/amp.8118.
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Blank, Ido, Lior Rokach, and Guy Shani. "Leveraging metadata to recommend keywords for academic papers." Journal of the Association for Information Science and Technology 67, no. 12 (2016): 3073–91. http://dx.doi.org/10.1002/asi.23571.
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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 solutions to calculate the tacit distortion of those tacit keywords. Furthermore, 4 algorithms were designed, including in picture tacit, textspeak tacit, fake paleography tacit and character tacit, which can extract the tacit keywords in text but traditional EKC could not. Owing to the increased number of extracted keywords, the recall of keywords raised and the precision of information filtering improved. Experiments show that the classification of tacit keywords in Chinese, the calculation of tacit distortion and the algorithms to tacit extract the keywords in Chinese, etc can effectively improve the performance of EKC and raise the recall of web filtering algorithms based on TEKC.
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Kim, Yoon, Hwang, and Jun. "Patent Keyword Analysis using Time Series and Copula Models." Applied Sciences 9, no. 19 (2019): 4071. http://dx.doi.org/10.3390/app9194071.
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The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.
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Fan, Zhi Guo, and Wen Long Zheng. "The Analysis of Research Hotspot of Chinese Cultural and Creative Industry - Based on Co-Words Method." Advanced Materials Research 798-799 (September 2013): 924–29. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.924.
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Based on the journal resource from the cqvip database, taking "cultural and creative industry" as the keyword, the paper retrieved all the relevant "CSSCI" documents from 1989 to 2012. After getting the keywords co-occurrence matrix through BIBCOMB software, the paper focused on the keywords analysis which was obtained by the software UCINET and NETDRAW. Then it comes to the conclusion, which shows that development countermeasures, cultural area, cultural creativity and influential factors are the hotspots in present research.
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Arie Edytia, Muhammad Heru, Zulhadi Sahputra, and Mirza Mirza. "ARCHITECTURAL DESIGN KEYWORDS OF INCEPTION SPACE." Malaysian Journal of Sustainable Environment 6, no. 2 (2019): 61. http://dx.doi.org/10.24191/myse.v6i2.8689.
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This paper explains the idea of inception space from Inception (2010), a movie directed by Christopher Nolan, to explore the inception space potential in designing architectural space. Inception space is an architectural space design mechanism that translates the essential experience of space users as an effort to implant idea in the form of positive emotions. In other words, the architectural space is a medium of inception to a space user or a target (mark). The main purpose of inception space design is to affect the target (mark) by planting the idea ‘secretly’. The target is unaware of the intervention and considers the idea presented itself. This process becomes the beginning of an idea to grow in one's mind the beginning of mindset and behavior change. In other words, architects or planners can apply this mechanism to design and influence users so that the design success rate can be improved. The main design keywords as part of the inception process are perception, memory, scenario, layer, and labyrinth. The development of design methods of inception space can be explored and applied to different targets and contexts by applying these design keywords. For example, this design mechanism can be applied to people with dementia who experience memory and visuospatial deficit through wayfinding programming.
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Pojanapunya, Punjaporn, and Richard Watson Todd. "The influence of the benchmark corpus on keyword analysis." Register Studies 3, no. 1 (2021): 88–114. http://dx.doi.org/10.1075/rs.19017.poj.
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Abstract The growing popularity of keyword analysis as an applied linguistics methodology has not been matched by an increase in the rigour with which the method is applied. While several studies have investigated the impact of choices made at certain stages of the keyword analysis process, the impact of the choice of benchmark corpus has largely been overlooked. In this paper, we compare a target corpus with several benchmark corpora and show that the keywords generated are different. We also show that certain characteristics of the keyword list and of the keywords themselves vary in relatively predictable ways depending on the benchmark corpus. These variations have implications for the choice of benchmark corpus and how the results of a keyword analysis should be interpreted. Analyzing the keywords from a comparison with a large general corpus or the keyword lists from multiple comparisons may be most appropriate for register studies.
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Oh, Se Eun, Shuai Li, and Nicholas Hopper. "Fingerprinting Keywords in Search Queries over Tor." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (2017): 251–70. http://dx.doi.org/10.1515/popets-2017-0048.
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AbstractSearch engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the contents of queries is to query the search engine over an anonymous network such as Tor.In this paper, we study the extent to which Website Fingerprinting can be extended to fingerprint individual queries or keywords to web applications, a task we call Keyword Fingerprinting (KF). We show that by augmenting traffic analysis using a two-stage approach with new task-specific feature sets, a passive network adversary can in many cases defeat the use of Tor to protect search engine queries.We explore three popular search engines, Google, Bing, and Duckduckgo, and several machine learning techniques with various experimental scenarios. Our experimental results show that KF can identify Google queries containing one of 300 targeted keywords with recall of 80% and precision of 91%, while identifying the specific monitored keyword among 300 search keywords with accuracy 48%. We also further investigate the factors that contribute to keyword fingerprintability to understand how search engines and users might protect against KF.
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Chundi, Parvathi, and Mahadevan Subramaniam. "Constructing Temporal Equivalence Partitionings for Keyword Sets." International Journal of Knowledge-Based Organizations 5, no. 3 (2015): 1–18. http://dx.doi.org/10.4018/ijkbo.2015070101.
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Identifying keyword associations from text and search sources is often used to facilitate many tasks such as understanding relationships among concepts, extracting relevant documents, matching advertisements to web pages, expanding user queries, etc. However, these keyword associations change continually change with time. In this paper, the authors define an equivalence relationship among keywords and develop methods to construct a temporal view of the equivalence relationship by constructing optimal temporal equivalence partitionings for keyword sets. They describe efficient algorithms to construct an optimal temporal equivalence partitioning for a keyword pair. They use the fact that the equivalence relationship is transitive to extend these algorithms to obtain an optimal temporal equivalence partitioning for a larger set of keywords. The authors show the effectiveness of the approach by constructing the temporal equivalence partitionings of several sets of keywords from the Multi-Domain Sentiment data set and the ICWS2009 Spinn3r data set.
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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 rapid word segmentation based on the research, analysis, improvement of traditional lexical maximum matching points, and analyze, realize extracting the keywords based on TFIDF(Term Frequency Inverse Document Frequency).
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I, Pratheek, and Joy Paulose. "Prediction of Answer Keywords using Char-RNN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2164. http://dx.doi.org/10.11591/ijece.v9i3.pp2164-2176.
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<p>Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.</p>
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Łaziński, Marek. "Klimat i jego pole leksykalne jako słowa klucze współczesnego dyskursu publicznego." Poradnik Językowy, no. 3/2021(782) (March 30, 2021): 7–16. http://dx.doi.org/10.33896/porj.2021.3.1.
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This paper presents increased frequency of vocabulary related to climate and health in public discourse and the position of this vocabulary in Polish and foreign word of the year contests. The fi rst part of the text discusses the notion of keywords, methods of their distinction, and word frequency monitoring works at the University of Warsaw. These works are composed of: 1) monitoring of the frequency of the vocabulary in daily newspapers against a comparable corpus covering 12 months, 2) selection of the word of the month from the most frequent words and describing it in philological terms, 3) word of the year contests using the most frequent words as propositions. The second part of the paper presents individual words from the lexical fi eld of climate selected as words of the month and of the year, such as upał (heat), nawałnica (a storm), smog (smog), drzewo (a tree), puszcza (a forest), klimat (climate). Part three demonstrates words from this lexical fi eld in Polish and foreign word of the year contests. The discussed lexical fi eld was divided into working categories: 1) “What the nature can do to a human being”, e.g. nawałnica (a storm), smog (smog), and 2) “What a human being does to the nature”, e.g. drzewo (a tree), puszcza (a forest) (tree cutting in a forest), klimat (climate) (climate change). The latter category gathers words with a greater symbolic power, more abstract, more appropriate as keywords in the long run. Keywords: keywords – frequency – word of the year contest – signifi cance of a word – climate
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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 keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.
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Kim, Jong-Min, and Sunghae Jun. "Integer-valued GARCH processes for Apple technology analysis." Industrial Management & Data Systems 117, no. 10 (2017): 2381–99. http://dx.doi.org/10.1108/imds-01-2017-0023.
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Purpose The keywords from patent documents contain a lot of information of technology. If we analyze the time series of keywords, we will be able to understand even more about technological evolution. The previous researches of time series processes in patent analysis were based on time series regression or the Box-Jenkins methodology. The methods dealt with continuous time series data. But the keyword time series data in patent analysis are not continuous, they are frequency integer values. So we need a new methodology for integer-valued time series model. The purpose of this paper is to propose modeling of integer-valued time series for patent analysis. Design/methodology/approach For modeling frequency data of keywords, the authors used integer-valued generalized autoregressive conditional heteroskedasticity model with Poisson and negative binomial distributions. Using the proposed models, the authors forecast the future trends of target keywords of Apple in order to know the future technology of Apple. Findings The authors carry out a case study to illustrate how the methodology can be applied to real problem. In this paper, the authors collect the patent documents issued by Apple, and analyze them to find the technological trend of Apple company. From the results of Apple case study, the authors can find which technological keywords are more important or critical in the entire structure of Apple’s technologies. Practical implications This paper contributes to the research and development planning for producing new products. The authors can develop and launch the innovative products to improve the technological competition of a company through complete understanding of the technological keyword trends. Originality/value The retrieved patent documents from the patent databases are not suitable for statistical analysis. So, the authors have to transform the documents into structured data suitable for statistics. In general, the structured data are a matrix consisting of patent (row) and keyword (column), and its element is an occurred frequency of a keyword in each patent. The data type is not continuous but discrete. However, in most researches, they were analyzed by statistical methods for continuous data. In this paper, the authors build a statistical model based on discrete data.
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Sarica, Serhad, Binyang Song, En Low, and Jianxi Luo. "Engineering Knowledge Graph for Keyword Discovery in Patent Search." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (2019): 2249–58. http://dx.doi.org/10.1017/dsi.2019.231.
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AbstractPatent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.
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Slomkowski, Stanislaw, Christopher M. Fellows, Roger C. Hiorns, et al. "List of keywords for polymer science (IUPAC Technical Report)." Pure and Applied Chemistry 91, no. 6 (2019): 997–1027. http://dx.doi.org/10.1515/pac-2018-0917.
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Abstract This paper provides a list of the most important terms from all areas of polymer science including polymer chemistry, polymer physics, polymer technology and polymer properties. These have been assembled into a representative list of terms that serves as an IUPAC recommended list of keywords for polymer science.
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Jun, Sunghae, and Seung Joo Lee. "Scientific and industrial keyword analysis using structured covariance and clustering." International Journal of Engineering & Technology 7, no. 3 (2018): 1501. http://dx.doi.org/10.14419/ijet.v7i3.12487.
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Using scientific and industrial keyword analysis (SIKA), many industrial companies have built their research and development (R&D) strategies for improving technological competitiveness in market. The technological keywords extracted from journal papers and patent documents are good resources for SIKA. In this paper, we use patent keyword data as scientific and industrial keywords for SIKA. A patent contains various information of developed technology such as patent title, abstract, date, citation, etc. Because the exclusive rights of technologies applied and registered to patent system are protected by patent law for a certain period. We also consider statistical methods for the SIKA. First we perform technology clustering using K-means clustering of technological patent keywords. Next we carry out the principal component analysis (PCA) from the clustering results. Using the first and second principal components, we obtain PCA plots for techno-logical clusters. So we can understand the technological structure of given and target technology from the PCA plot results. Combing the technology clustering and PCA plots, we propose a method of SIKA to build valuable R&D strategy of company. To illustrate how the proposed method could be applied to real problem, we make experiments using many technological keywords for given technology field.
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ALI, FAIROUZ SHER, and SONG FENG LU. "PUBLIC KEY ENCRYPTION WITH CONJUNCTIVE FIELD FREE KEYWORD SEARCH SCHEME." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 14 (2016): 7423–34. http://dx.doi.org/10.24297/ijct.v15i14.834.
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Searchable encryption allows a remote server to search over encrypted documents without knowing the sensitive data contents. Prior searchable symmetric encryption schemes focus on single keyword search. Conjunctive Keyword Searches (CKS) schemes improve system usability by retrieving the matched documents. In this type of search, the user has to repeatedly perform the search protocol for many times. Most of existent (CKS) schemes use conjunctive keyword searches with fixed position keyword fields, this type of search is not useful for many applications, such as unstructured text. In our paper, we propose a new public key encryption scheme based on bilinear pairings, the scheme supports conjunctive keyword search queries on encrypted data without needing to specify the positions of the keywords where the keywords can be in any arbitrary order. Instead of giving the server one trapdoor for each keyword in the conjunction set, we use a bilinear map per a set of combined keywords to make them regarded as one keyword. In another meaning, the proposed method will retrieve the data in one round of communication between the user and server. Furthermore, the search process could not reveal any information about the number of keywords in the query expression. Through analysis section we determine how such scheme could be used to guarantee fast and secure access to the database.
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Yoon, Hee-Young, and Il-Youp Kwak. "The Association Modeling on Keywords and Documents of Korea International Trade Research using Paper Abstract data." Korean Academy Of International Commerce 35, no. 2 (2020): 45–64. http://dx.doi.org/10.18104/kaic.2020.35.2.45.
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Klarin-Henricson, Anja. "KEYWORDS: Bleached kraft pulp, Deacetylation, Drying, Galactoglucomannans, Galactomannans, Paper strength, Peroxide bleaching, Spruce TMP, Water retention." Nordic Pulp & Paper Research Journal 19, no. 2 (2004): 245–49. http://dx.doi.org/10.3183/npprj-2004-19-02-p245-249.
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Bhardwaj, Akansha, Jie Yang, and Philippe Cudré-Mauroux. "A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2451–58. http://dx.doi.org/10.1609/aaai.v34i03.5626.
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Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation of the distribution – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.
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Zhang, Yu, Yin Li, and Yifan Wang. "Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting." Applied Sciences 9, no. 18 (2019): 3655. http://dx.doi.org/10.3390/app9183655.
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Searchable public key encryption supporting conjunctive keywords search (SPE-CKS) enables data users to retrieve the encrypted data of interest from an untrusted server. Based on SPE-CKS, one can realize a multi-keywords search over the encrypted e-mails. In this paper, we propose an efficient SPE-CKS scheme by utilizing a keyword conversion method and the bilinear map technique. Our scheme is proven to be secure against chosen keyword attack under a standard security definition and can also withstand the keywords guessing attack. Furthermore, we design an experiment over a real world e-mail dataset to illustrate that our scheme has a better performance on time and space complexities than the previous schemes. A detailed analysis shows that our scheme is very practical for the encrypted e-mail system in the mobile cloud environment.
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Yang, Weidong, Fei Fang, Nan Li, and Zhongyu (Joan) Lu. "XKFitler." International Journal of Information Retrieval Research 1, no. 1 (2011): 1–18. http://dx.doi.org/10.4018/ijirr.2011010101.
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Most existing XML stream processing systems adopt full structured query languages, such as XPath or XQuery, but they are difficult for ordinary users to learn and use. Keyword search is a user-friendly information discovery technique that has been extensively studied for text documents. This paper presents an XML stream filter system called XKFitler, which is the first system for supporting keyword search over XML stream. In XKFitler, the concepts of XLCA (eXclusive Lowest Common Ancestor) and XLCA Connecting Tree (XLCACT) are used to define the search semantic and results of keywords, and present an approach to filter XML stream according to keywords. The prototype XKFilter is implemented in the experiments.
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Moreno, Edward David, Maria Elena Leon Olave, and Paulo Afonso. "Prospecting the Impact of New Business based on Project Keywords." International Journal for Innovation Education and Research 8, no. 4 (2020): 125–33. http://dx.doi.org/10.31686/ijier.vol8.iss4.2269.
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In this paper we present and use the ITW-Index – this name was given in reference to the term Technology Watch, which is a technique of observation, analysis and identification of possible opportunities and threats, linked to the method of technological forecasting called Monitoring and Intelligence Systems. The purpose of TW-Index is to provide the user the capacity of monitoring and identifying whether certain terms which are used for defining a technology or research are currently been searched and used in the Internet. For this, we used as a basis the Google Trends. So, in this paper we present three contributions: (i) the concept of the TW-index, (ii) options for evaluating and getting a value for the TW-index applied in two different examples, and (iii) we measure and explain the means of TW-index when it is applied in two ideas for creating new business into the IdeaLAB program at University of Minho, Portugal.
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Duguid, Alison. "Newspaper discourse informalisation: a diachronic comparison from keywords." Corpora 5, no. 2 (2010): 109–38. http://dx.doi.org/10.3366/cor.2010.0102.
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In this paper, I provide an overview of certain types of salient items found in the keyword lists of the SiBol 1993 and SiBol 2005 corpora with the objective of diachronic analysis of a particular text type, namely, that of British broadsheet newspapers. I analysed the keyword lists (see Partington, 2010 : Section 2 ) in search of items that could be assigned to semantic sets, which could be glossed as hyperbole, vagueness and informal evaluation. The appearance of these sets in the keywords for 2005 seems to point to changes over time in newspaper prose style. The newspapers under consideration thus appear to have altered both in their function and in their relationship with their readership; and this is reflected in the salient lexis and its contexts of use. An increase in conversational and informal styles emerges, along with a notable increase in a particular kind of evaluative and promotional language as a result of a proportional increase in soft news, supplements and reviews.
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Pesta, Bryan, John Fuerst, and Emil Kirkegaard. "Bibliometric Keyword Analysis across Seventeen Years (2000–2016) of Intelligence Articles." Journal of Intelligence 6, no. 4 (2018): 46. http://dx.doi.org/10.3390/jintelligence6040046.
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An article’s keywords are distinct because they represent what authors feel are the most important words in their papers. Combined, they can even shed light on which research topics in a field are popular (or less so). Here we conducted bibliometric keyword analyses of articles published in the journal, Intelligence (2000–2016). The article set comprised 916 keyword-containing papers. First, we analyzed frequencies to determine which keywords were most/least popular. Second, we analyzed Web of Science (WOS) citation counts for the articles listing each keyword and we ran regression analyses to examine the effect of keyword categories on citation counts. Third, we looked at how citation counts varied across time. For the frequency analysis, “g factor”, “psychometrics/statistics”, and “education” emerged as the keywords with the highest counts. Conversely, the WOS citation analysis showed that papers with the keywords “spatial ability”, “factor analysis”, and “executive function” had the highest mean citation values. We offer tentative explanations for the discrepant results across frequencies and citations. The analysis across time revealed several keywords that increased (or decreased) in frequency over 17 years. We end by discussing how bibliometric keyword analysis can detect research trends in the field, both now and in the past.
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Kettunen, Kimmo. "Reductive and generative approaches to management of morphological variation of keywords in monolingual information retrieval." Journal of Documentation 65, no. 2 (2009): 267–90. http://dx.doi.org/10.1108/00220410910937615.
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PurposeThe purpose of this article is to discuss advantages and disadvantages of various means to manage morphological variation of keywords in monolingual information retrieval.Design/methodology/approachThe authors present a compilation of query results from 11 mostly European languages and a new general classification of the language dependent techniques for management of morphological variation. Variants of the different techniques are compared in some detail in terms of retrieval effectiveness and other criteria. The paper consists mainly of an overview of different management methods for keyword variation in information retrieval. Typical IR retrieval results of 11 languages and a new classification for keyword management methods are also presented.FindingsThe main results of the paper are an overall comparison of reductive and generative keyword management methods in terms of retrieval effectiveness and other broader criteria.Originality/valueThe paper is of value to anyone who wants to get an overall picture of keyword management techniques used in IR.
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Holborow, Marnie. "Applied Linguistics in the Neoliberal University: Ideological keywords and social agency." Applied Linguistics Review 4, no. 2 (2013): 229–57. http://dx.doi.org/10.1515/applirev-2013-0011.
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AbstractNeoliberalism and neoliberal ideology has only recently begun to gain attention within applied linguistics. This paper seeks to contribute to this development with a focus on neoliberal keywords in official texts. The ideological content of these keywords can best be understood within the political project of neoliberalism and within the political economy of contemporary capitalism. Studies which have highlighted the marketization of institutional discourse have analysed this phenomenon from a discourse-based perspective, rather than seeing neoliberal ideology in language as a contradictory manifestation of wider social relations in periods of social crises. The appearance of ideology in language, this paper holds, is unstable, unfinished, unpredictable and dependent for meaning on what Dell Hymes characterised as the “persistent” social context. The ideology of neoliberalism, for all its apparent hegemony, is not guaranteed full consent, and this applies also to its presence in language. The question of social agency is crucial to understanding the social dynamic and unpredictability of ideology in language, both in terms of who produces neoliberal keywords and how they are received and understood. This paper argues that international think tanks, articulating the interests of capital, act as powerful keyword standardisers and their influence will be examined in the production of texts in the Irish university context. However, neoliberal keywords, in certain conjunctures, will also be contested, as will be shown. The paper concludes that applied linguistics is uniquely placed to both critique and challenge neoliberal keywords in the university and that such a challenge has the potential to find wider political resonance as governments, amid continuing economic recession, recharge the ideology of neoliberalism.
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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