کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382582 660770 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Language independent semantic kernels for short-text classification
ترجمه فارسی عنوان
زبان هسته معنایی مستقل برای طبقه بندی متن کوتاه
کلمات کلیدی
طبقه بندی مدارک کوتاه، روش کرنل، اندازه گیری مشابهی هسته معنایی مستقل زبان
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Kernels for short-text classification without language dependencies were proposed.
• Three levels of annotations were used for precise calculation of semantic similarity.
• The performances were evaluated by using real-world English and Korean datasets.

Short-text classification is increasingly used in a wide range of applications. However, it still remains a challenging problem due to the insufficient nature of word occurrences in short-text documents, although some recently developed methods which exploit syntactic or semantic information have enhanced performance in short-text classification. The language-dependency problem, however, caused by the heavy use of grammatical tags and lexical databases, is considered the major drawback of the previous methods when they are applied to applications in diverse languages. In this article, we propose a novel kernel, called language independent semantic (LIS) kernel, which is able to effectively compute the similarity between short-text documents without using grammatical tags and lexical databases. From the experiment results on English and Korean datasets, it is shown that the LIS kernel has better performance than several existing kernels.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 41, Issue 2, 1 February 2014, Pages 735–743
نویسندگان
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