کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6866712 | 678246 | 2014 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fuzzy deep belief networks for semi-supervised sentiment classification
ترجمه فارسی عنوان
شبکه های اعتقادی عمیق فازی برای طبقه بندی احساسات نیمه نظارت
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کلمات کلیدی
نظارت بر یادگیری، یادگیری عمیق، مجموعه های فازی طبقه بندی احساسات،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 131, 5 May 2014, Pages 312-322
Journal: Neurocomputing - Volume 131, 5 May 2014, Pages 312-322
نویسندگان
Shusen Zhou, Qingcai Chen, Xiaolong Wang,