کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6854393 | 1437428 | 2016 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling semantic compositionality of relational patterns
ترجمه فارسی عنوان
مدلسازی ترکیبات معناشناختی الگوهای ارتباطی
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کلمات کلیدی
کسب دانش، پردازش زبان طبیعی، استخراج رابطه، شبکه عصبی بازگشتی تعبیه کلمه ترکیبات معنایی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Vector representation is a common approach for expressing the meaning of a relational pattern. Most previous work obtained a vector of a relational pattern based on the distribution of its context words (e.g., arguments of the relational pattern), regarding the pattern as a single 'word'. However, this approach suffers from the data sparseness problem, because relational patterns are productive, i.e., produced by combinations of words. To address this problem, we propose a novel method for computing the meaning of a relational pattern based on the semantic compositionality of constituent words. We extend the Skip-gram model (Mikolov et al., 2013) to handle semantic compositions of relational patterns using recursive neural networks. The experimental results show the superiority of the proposed method for modeling the meanings of relational patterns, and demonstrate the contribution of this work to the task of relation extraction.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 50, April 2016, Pages 256-264
Journal: Engineering Applications of Artificial Intelligence - Volume 50, April 2016, Pages 256-264
نویسندگان
Sho Takase, Naoaki Okazaki, Kentaro Inui,