دانلود مقاله انگلیسی رایگان:تأثیر یادگیری عمیق در طبقه بندی اسناد با استفاده از نمایش های غنی معنایی - 2019
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  • The impact of deep learning on document classification using semantically rich representations The impact of deep learning on document classification using semantically rich representations
    The impact of deep learning on document classification using semantically rich representations

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    The impact of deep learning on document classification using semantically rich representations


    ترجمه فارسی عنوان مقاله:

    تأثیر یادگیری عمیق در طبقه بندی اسناد با استفاده از نمایش های غنی معنایی


    منبع:

    Sciencedirect - Elsevier - Information Processing and Management, 56 (2019) 1618-1632: doi:10:1016/j:ipm:2019:05:003


    نویسنده:

    Zenun Kastrati⁎, Ali Shariq Imran, Sule Yildirim Yayilgan


    چکیده انگلیسی:

    This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used. Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.
    Keywords: Document representation | Document classification | Deep learning | Ontology | Machine learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 15
    حجم فایل: 1964 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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