scholarly journals Leveraging full-text article exploration for citation analysis

2021 ◽  
Author(s):  
Moreno La Quatra ◽  
Luca Cagliero ◽  
Elena Baralis
Keyword(s):  
Correlation Analysis ◽  
Citation Analysis ◽  
Full Text ◽  
Data Driven ◽  
Full Text Article ◽  
Classification Approach ◽  
Citation Context ◽  
External Sources ◽  
Textual Content

AbstractScientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.

scholarly journals Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga
Keyword(s):  
Correlation Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Cross Correlation ◽  
Data Driven ◽  
Cross Correlation Analysis ◽  
Data Driven Approach

Citation context-based topic models: discovering cited and citing topics from full text

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lixue Zou ◽  
Xiwen Liu ◽  
Wray Buntine ◽  
Yanli Liu
Keyword(s):  
Full Text ◽  
Topic Models ◽  
Content Type ◽  
Reference Information ◽  
Citation Context ◽  
Comparable Performance ◽  
Automatic Text Analysis ◽  
Collapsed Gibbs Sampling ◽  
Source Of Information ◽  
Automatic Text

PurposeFull text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.Design/methodology/approachThe authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.FindingsThe results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.Originality/valueThe automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.

scholarly journals Designing web-scale discovery systems using the VuFind open source software

2018 ◽  
Vol 35 (3) ◽  
pp. 16-22 ◽  
Author(s):  
Bijan Kumar Roy ◽  
Subal Chandra Biswas ◽  
Parthasarathi Mukhopadhyay
Keyword(s):  
Open Source ◽  
Full Text ◽  
Resource Discovery ◽  
Content Type ◽  
Discovery System ◽  
Proposed Model ◽  
One Stop ◽  
External Sources ◽  
Text Searching ◽  
Software Standards

Purpose This paper aims to provide an overview of the emergence of resource discovery systems and services along with their advantages and best practices including current landscapes. It reports the development of a resource discovery system by using the “VuFind” software and describes other technological tools, software, standards and protocols required for the development of the prototype. Design/methodology/approach This paper describes the process of integrating VuFind (resource discovery tool) with Koha (integrated library system), DSpace (repository software) and Apache Tika (as full-text extractor for full-text searching), etc. Findings The proposed model performs like other existing commercial and open source Web-scale resource discovery systems and is capable of harvesting resources from different subscribed or external sources replacing a library’s OPAC. Originality/value This discovery system is an important add-on to designing a one-stop access in place of the existing retrieval silos in libraries. This system is capable of indexing a variety of content within and beyond library collections. This work may help library professionals and administrators in designing their discovery system, as well as vendors to improve their products, to provide different library-friendly services.

scholarly journals Terapi Non Farmakalogis untuk Mengurangi Kecemasan pada Pasien Kanker dengan Kemoterapi: A Systematic Review

2020 ◽  
Vol 11 (4) ◽  
pp. 334
Author(s):  
Andrik Hermanto ◽  
Tintin Sukartini ◽  
Esti Yunitasari
Keyword(s):  
Systematic Review ◽  
Virtual Reality ◽  
Cancer Patients ◽  
Full Text ◽  
Guided Imagery ◽  
Subcutaneous Tissue ◽  
Full Text Article ◽  
Inclusion Criteria ◽  
Tissue Necrosis ◽  
Keywords Cancer

Background: Anxiety will affect the cancer patient's physiology and decrease the body's immune system, so that intervention is needed to alleviate anxiety. Objective: To find out various non-pharmacalogical therapies to reduce anxiety in cancer patients with chemotherapy. Method: The database used in this study was scopus, proquest and pubmed were limited to the last 5 years of publication from 2016 to 2020, full-text article and in english. The keywords used were "cancer" and "anxiety". This systematic review uses 10 articles that fit the inclusion criteria. Results: nonpharmacological management of cancer patients to reduce anxiety includes music therapy, autogenic training, mindfulness programs, virtual reality, guided imagery and progressive muscle training. Non-pharmacalogical therapy functions to reduce anxiety in cancer patients with chemotherapy and reduce various kinds of side effects such as anaemia, thrombocytopenia, leucopenia, nausea and vomiting, alopecia (hair loss), stomatitis, allergic reactions, neurotoxic, and extravasation (discharge of vesicle or irritant drugs to the patient) subcutaneous tissue resulting in pain, tissue necrosis, and tissue ulceration). Keywords: cancer; anxiety; nonpharmacologic therapy ABSTRAK Latar belakang: Cemas akan mempengaruhi psikologis pasien kanker dan menurunkan sistem imun tubuh, sehingga dibutuhkan intervensi yang dapat meringankan kecemasan. Tujuan: Untuk mengetahui berbagai macam terapi non farmakalogis untuk mengurangi kecemasan pada pasien kanker dengan kemoterapi. Metode: Database yang digunakan dalam studi ini adalah Scopus, Proquest dan Pubmed terbatas untuk publikasi 5 tahun terakhir dari 2016 hingga 2020, full text article dan berbahasa Inggris. Kata kunci yang digunakan adalah “cancer” AND “anxiety”. Systematic review ini menggunakan 10 artikel yang sesuai dengan kriteria inklusi Hasil: Tatalaksana nonfarmakologi pada pasien kanker untuk mengurangi cemas antara lain meliputi terapi musik, latihan autogenik, minfullnes program, virtual reality, guided imagery dan latihan otot progresif. Terapi non farmakalogis berfungsi untuk mengurangi kecemasan pada pasien kanker dengan kemoterapi dan mengurangi berbagai macam efek samping seperti anemia, trombositopenia, leucopenia, mual dan muntah, alopesia (rambut rontok), stomatitis, reaksialergi, neurotoksik, dan ekstravasasi (keluarnya obat vesikan atau iritan ke jaringan subkutan yang berakibat timbulnya rasa nyeri, nekrosis jaringan, dan ulserasi jaringan). Kata kunci: kanker; kecemasan; terapi nonfarmakologi

scholarly journals Research Productivity and Its Relationship to Library Collections

2020 ◽  
Vol 15 (4) ◽  
pp. 16-32
Author(s):  
Sandra L. De Groote ◽  
Beyza Aksu Dunya ◽  
Jung Mi Scoulas ◽  
Mary M. Case
Keyword(s):  
Research And Development ◽  
Multiple Regression ◽  
Full Text ◽  
Research Productivity ◽  
Full Text Article ◽  
Faculty Productivity ◽  
Hierarchical Multiple Regression ◽  
Library Material ◽  
Research Expenditures ◽  
Library Collections

Objective – The purpose of this study was to explore in the current academic library environment, the relationship between library collections data (collections’ size, expenditures, and usage) and faculty productivity (scholarly output). The researchers also examined the degree to which new and existing library metrics predict faculty productivity. Methods – Demographic data (e.g., faculty size, student size, research and development expenditures), library budget data (e.g., collection expenditures), collection use data (e.g., full-text article requests and database searches), and publication output for 81 doctoral granting universities in the United States were collected to explore potential relationships between research productivity, collection use, library budgets, collection size, and research expenditures using partial correlations. A hierarchical multiple regression was also used to ascertain the significance of certain predictors of research productivity (publications). Results – A correlation existed between the number of publications (research productivity) and library expenditures (total library expenditures, total library material expenditures, and ongoing library resource expenditures), collection size (volumes, titles, and ebooks), use of collection (full-text article requests and total number of references in the articles), and research and development expenditures. Another key finding from the hierarchical multiple regression analysis showed that full-text article requests were the best predictor of research productivity, which uniquely explained 10.2% of the variation in publication. Conclusion – The primary findings were that full-text article requests, followed by library material expenditures and research expenditures, were found to be the best predictor of research productivity as measured by articles published.

Sign in / Sign up

Export Citation Format

Share Document