کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6857248 | 661905 | 2016 | 23 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Exploiting flexible-constrained K-means clustering with word embedding for aspect-phrase grouping
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Aspect-phrase grouping is an important task for aspect finding in sentiment analysis. Most existing methods for this task are based on a window-context model, which assumes that the same aspect has similar co-occurrence contexts. This model does not always work well in practice. In this paper, we develop a novel weighted context representation model based on semantic relevance, which exploits word embedding method to represent aspect-phrase. And we encode the lexical knowledge as constraints with a degree of belief, and further propose a flexible-constrained K-means algorithm to cluster aspect-phrases. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 367â368, 1 November 2016, Pages 689-699
Journal: Information Sciences - Volumes 367â368, 1 November 2016, Pages 689-699
نویسندگان
Shufeng Xiong, Donghong Ji,