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
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Exploiting flexible-constrained K-means clustering with word embedding for aspect-phrase grouping
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 367–368, 1 November 2016, Pages 689-699
نویسندگان
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