Abstract-Trie is one of the data structures for keyword matching. The trie is used in natural language processing, IP address routing, and so on. It is represented by the matrix form, the link form, the double array, and LOUDS. The double array combines retrieval speed of the matrix form with compactness of the list form. LOUDS is a succinct data structure using bit-string. Retrieval speed of LOUDS is not faster than that of double array, but the dictionary size is smaller. This paper proposes a compression data structure of the double array by dividing trie into each depth and removing the BASE array from that double array. From experimental results, the retrieval speed was almost the same as double array, and the size of the presented method was the most compact in other methods including LOUDS for a large set of keywords with fixed length.

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Abstract-For context-based recommendation systems, it is necessary to detect affirmative and negative intentions from answers. However, traditional studies cannot determine these intentions from indirect speech acts.In order to determine these intentions from indirect speech acts, this paper defines a recommendation tree and proposes an algorithm of deriving intentions of indirect speech acts by the tree. In the proposed method, a recommendation condition (RC) is introduced and it is classified into a required RC, a selectable RC, and a not-selectable RC. The recommendation tree is constructed by nodes and edges corresponding to these three conditions. The deriving algorithm determines affirmative and negative intentions of indirect speech acts by tracing the trees.From experimental results, it is verified that the accuracy of the proposed method is about 40 points higher than the traditional method.

Studies of robots which aim to entertain and to be conversational partners of the live-alone become very important. The robots are classified into DBC (Dialogue-Based Communication) robots and TBC (Touch-Based Communication) robots. DBC robots have an effect to be conversational partners. A typical application of TBC robots is Paro (a baby harp seal robot) which has an effect to entertain humans. The combination of DBC and TBC will be able to achieve both a conversational ability and an entertaining effect, but there is no study of combination of DBC and TBC. This paper proposes a response algorithm that can combine conversational information and touch information from humans. Criterions for estimation are defined as follows: FFV (Familiarity Factor Value), EFV (Enjoyment Factor Value), CR (Concentration Rate), ER (Expression Rate), and RR (Recognition Rate). FFV and EFV are total ratings for questionnaire related to familiarity and enjoyment factors, respectively. CR measures attention for humans. ER is the interest of communication with robots by representing Level 2 (laugh with opening one’s mouse), Level 1 (smile), and Level 0 (expressionless). RR is recognition ability for voices and touch actions. From the experiment for impressions of robot responses with 11 subjects, it turns out that the proposed method with combination of DBC and TBC is improved by 20.7 points in FFV, and by 12.6 points in EFV compared to only TBC. From the robot communication experiment, it turns out that the proposed method is improved by 8 points in the ER, by 5.3 points in the ER with Level 2, and by 24.5 points in the RR compared to only DBC

For context-based recommendation systems, it is important to determine intentions from indirect speech acts.An algorithm of deriving intentions from indirect speech acts has been proposed, but the algorithm included unclear portions and there were no important experimental results for kinds of speech acts. Therefore, this paper proposes an improved algorithm and two experimental observations are discussed for accuracies and kinds of answers in indirect speech acts.Logical formulas are rewritten to if-else statements and the number of conditions is reduced from 24 to 8 in the algorithm. From experimental results, it is verified that the correct rate of the proposed method is 48.2 points higher than the one of the traditional method in indirect speech acts. Answers of "what" most include indirect speech acts and the accuracy of the proposed method is 53.8 points higher than the traditional one in them.