Current Issue
    , Volume 3 Issue 10 Previous Issue    Next Issue
    For Selected: View Abstracts Toggle Thumbnails
    Research Methods and Technologies for Information Science from Process-Problem Perspective: Case Study of Public Opinion
    Hui Zhu,Hao Wang,Chengzhi Zhang
    2019, 3 (10): 2-11.  DOI: 10.11925/infotech.2096-3467.2019.0028
    Abstract   HTML ( 24 PDF (641KB) ( 81 )

    [Objective] This paper explores large-scale information science literature, aiming to better examine research methods and technologies in this field and organize them from the“process-problem” perspective. [Methods] Firstly, we analyzed the information lifecycles and related research questions. Secondly, we grouped and labeled literature by research questions. Thirdly, we extracted terms of research methods and technologies based on dictionary and templates. Finally, we organized the terms from the “process-problem” perspective. [Results] The F1 value of the proposed method reached 90.91%. [Limitations] We collected experimental data only from the CNKI database and the templates for extracting terms need improvements. [Conclusions] We could extract terms of research methods and technologies with the proposed model simultaneously and effectively.

    Figures and Tables | References | Related Articles | Metrics
    Extracting Sentences of Research Originality from Full Text Academic Articles
    Chengzhi Zhang,Zheng Li
    2019, 3 (10): 12-18.  DOI: 10.11925/infotech.2096-3467.2019.0055
    Abstract   HTML ( 22 PDF (562KB) ( 62 )

    [Objective] This paper analyzes full texts of academic articles, aiming to extract sentences of research originality as well as, exploring their characteristics. [Methods] We used full-text journal papers in the field of library, information and archives as experiment data. Then, we chose mark words, created extraction rules for sentences of research originality. Finally, we analyzed distribution of these sentences with the mark words, types, and locations. [Results] The extracted sentences were mainly divided into six categories, and most of them appeared in the top 24.8% section of each article. [Limitations] The proposed sentence extraction method needs to be optimized. [Conclusions] Sentences of research originality in the field of library, information and archives focus on concepts and theories. The categories and distributions of these sentences are various among different journals.

    Figures and Tables | References | Related Articles | Metrics
    Entity Recognition of Intelligence Method Based on Deep Learning: Taking Area of Security Intelligence for Example
    Lianjie Xiao,Tao Meng,Wei Wang,Zhixiang Wu
    2019, 3 (10): 20-28.  DOI: 10.11925/infotech.2096-3467.2018.1199
    Abstract   HTML ( 45 PDF (1325KB) ( 88 )

    [Objective] This paper provides directions for a new scholarly system, aiming to identify and summarize intelligence analysis methods for security intelligence. [Methods] Firstly, we retrieved full-text security intelligence literature, and tagged them using Character-level method. Then, we constructed the corpus for the extraction of intelligence analysis methods. Finally, we compared the performance of two deep learning models with the experimental data. [Results] For the BiLSTM model, the precision, recall and F1 values were 81.71%, 77.26%, and 79.36% respectively. For the BiLSTM-CRF model, the precision, recall and F1 values were 84.71%, 79.25%, and 81.83%. [Limitations] The pronouns that represent intelligence analysis methods are not taken into consideration. [Conclusions] We could use deep learning model to extract intelligence analysis methods for security intelligence.

    Figures and Tables | References | Related Articles | Metrics
    System Analysis and Design for Methodological Entities Extraction in Full Text of Academic Literature
    Hao Xu,Xuefang Zhu,Chengzhi Zhang,Chuan Jiang
    2019, 3 (10): 29-36.  DOI: 10.11925/infotech.2096-3467.2019.0069
    Abstract   HTML ( 18 PDF (1446KB) ( 85 )

    [Objective] This paper proposes a new system to extract methodological entities from the full texts of academic literature, aiming to identify their indexing features and usages. [Methods] Firstly, we extracted feature sentences and methodological entities based on dictionaries, rules, and manual annotations. Then, we implemented a methodology knowledge extraction module with the help of Microsoft Visual Studio 2012 and SQL Server 2012. [Results] The precision of extracting methodological features was 76%, while the recall rate was greater than 42%. Each feature sentence had 1.42 method entities on average. The formal indexing ratio for methodological entities was less than 27%, while the ratio for feature sentences was less than 35%. We also found low formal indexing rate for subject-specific methodological entities. [Limitations] This system’s recall and precision rates were not very satisfactory. The manual workload was intensive for entity extraction and did not include the semantic features. [Conclusions] The proposed method has inter-disciplinary versatility and helps us explore