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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Analyzing CV/resume using natural language processing and machine learning

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    14101061,14101171_CSE.pdf (1.298Mb)
    Date
    2017
    Publisher
    BRAC University
    Author
    Reza, Md. Tanzim
    Zaman, Md. Sakib
    Metadata
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    URI
    http://hdl.handle.net/10361/9480
    Abstract
    This paper proposes a model of extracting important information from the semi-structured text format in a curriculum vitae or resume and ranking it according to the preference of the associated company and requirements. In order to achieve the desired goal, the entire process has been divided into 3 basic segments. The first segment consists of segmenting the entire CV / Resume based on the topic of each part, the second segment consists of extracting data in structured form from the unstructured data and the final segment consists of evaluating the structured data by decision tree algorithm and training the system. The structured data extraction process is done by segmenting the entire CV / Resume by converting it to HTML. After the conversion to structured data, decision tree algorithm techniques are used to classify the input into different categories based on qualifications and then the data with positive weight is used to train the system for future benefit. Finally, classifier algorithm apart from decision tree such as logistic regression is used to compare the classification result.
    Keywords
    Machine learning; CV; Resume; Natural language; NLP; JSON; ID3
     
    Description
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references.
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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