کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6901298 | 1446493 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
TIDM: Topic-Specific Information Detection Model
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Nowadays information control and detection on the social network have become a problem that we should solve as soon as possible. Unfortunately, due to the informal expressions, detecting the massive data on the internet is a big challenge based on the traditional text mining methods such as Topic Model. In our paper, we propose a simple 4-Tuple Structure instead of the raw text event which usually contains many meaningless words. Using the word embedding technique, we propose the Topic-Specific Information Detection Model (TIDM) for detecting the specific information. For training the words and idiomatic phrases, we adopt the supervise learning technique: manually constructing a specific Semantic Dataset for training our model. Our experiments based on the Amazon Reviews demonstrate that the TIDM can effectively detect and recognize the information.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 122, 2017, Pages 229-236
Journal: Procedia Computer Science - Volume 122, 2017, Pages 229-236
نویسندگان
Wen Xu, Jing He, Bo Mao, Youtao Li, Peiqun Liu, Zhiwang Zhang, Jie Cao,