A vector-based method for efficiently representing multivariate environmental information

This paper outlines our approach for describing multivariate environmental information such as weather as it might be characterized by humans using connotations and delicate nuances. The purpose of this research is to achieve smooth human-machine spoken dialogue. The key feature of our approach is the use of a vector-based method, a widely used technique in Information Retrieval research. To date, no system concept as mentioned or the use of the vector-space in such a system has been reported. First, phrases implicitly expressing environmental information are collected to make the list of candidate phrases. Next the environmental conditions of that time are entered from the GUI. The phrases and the newly entered information are represented as vectors of component values representing such information. By computing the similarity (relatedness) between the given condition and each candidate phrase, the phrase that best represents the given condition is chosen. Two experimental systems that work in Japanese have been developed to evaluate the validity of our method. The first system selects the appropriate phrase to represent the weather implicitly with a minimum number of phrases and not by lining up measured values. The other system selects poetic phrases that most appropriately represent the environmental conditions, including weather information and emotional states. The result of our evaluation has shown that both systems successfully selected the suitable phrases for representing given environmental conditions.