Word vectorization using relations among words for neural network

Hajime Hotta, Masanobu Kittaka, Masafumi Hagiwara

研究成果: Article査読

9 被引用数 (Scopus)


In this paper, we propose a new vectorization method for a new generation of computational intelligence including neural networks and natural language processing. In recent years, various techniques of word vectorization have been proposed, many of which rely on the preparation of dictionaries. However, these techniques don't consider the symbol grounding problem for unknown types of data, which is one of the most fundamental issues on artificial intelligence. In order to avoid the symbol-grounding problem, pattern processing based methods, such as neural networks, are often used in various studies on self-directive systems and algorithms, and the merit of neural network is not exception in the natural language processing. The proposed method is a converter from one word input to one real-valued vector, whose algorithm is inspired by neural network architecture. he merits of the method are as follows: (1) the method requires no specific knowledge of linguistics e.g. word classes or grammatical one; (2) the method is a sequence learning technique and it can learn additional knowledge. The experiment showed the efficiency of word vectorization in terms of similarity measurement.

ジャーナルIEEJ Transactions on Electronics, Information and Systems
出版ステータスPublished - 2010

ASJC Scopus subject areas

  • 電子工学および電気工学


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