sis 18: e1

Research Article

Word Embedding and String-Matching Techniques for Automobile Entity Name Identification from Web Reviews

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  • @ARTICLE{10.4108/eai.14-5-2021.169918,
        author={Satanu Maity and Nilanjana Das and Mukta Majumder and Dibya Ranjan Dasadhikary},
        title={Word Embedding and String-Matching Techniques for Automobile Entity Name Identification from Web Reviews},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={5},
        keywords={Noisy Name Identification, Automobile Discussion Forum, Machine Learning, Support Vector Machine, Conditional Random Field, Word Embedding, String Matching},
        doi={10.4108/eai.14-5-2021.169918}
    }
    
  • Satanu Maity
    Nilanjana Das
    Mukta Majumder
    Dibya Ranjan Dasadhikary
    Year: 2021
    Word Embedding and String-Matching Techniques for Automobile Entity Name Identification from Web Reviews
    SIS
    EAI
    DOI: 10.4108/eai.14-5-2021.169918
Satanu Maity1, Nilanjana Das2, Mukta Majumder3,*, Dibya Ranjan Dasadhikary4
  • 1: Department of Computer Application, Bengal School of Technology and Management, Hooghly, India
  • 2: Midnapore Zone, WBSEDCL, Midnapore, India
  • 3: Department of Computer Science and Application, University of North Bengal, Siliguri, India
  • 4: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India
*Contact email: mukta_jgec_it_4@yahoo.co.in

Abstract

With the huge popularity of Internet, various types of information on a wide range of domains are floating over different social media platforms. To extract this information for using in diverse natural language processing applications, identifying the names is prerequisite. A study is presented here, to identify automobile names from noisy web reviews by exploring two widely used machine learning algorithms, Conditional Random Field and Support Vector Machine. The accuracy of machine learning classifiers radically rely on size and quality of training data which has been prepared manually by extracting discussion forum corpus; the task is time consuming and laborious; hence to leverage this word embedding is adopted. Though it enhances the system’s performance but is unable to spot noisy names which occur in web reviews. Next, a gazetteer based string matching technique is proposed, it recognizes a new set of noisy automobile entities, resulting considerable improvement in accuracy.