This paper proposes a method for conversational agents to change topic during conversation based on trend information. Detection of dialogue breakdown is one of important research themes for developing conversational agents. However, few studies have been done on how to do after detecting dialogue breakdown. The proposed method finds a topic that relates with current topic in terms of temporal trend by using context search engine. This paper also proposes a method to detect dialogue breakdown based on response time by a human. The results of preliminary experiments are reported to examine the possibility of the proposed methods.

This paper introduces a method for related term suggestion for cooking recipe search. Our method recognizes the document structure of recipe data and allows users to specify which word context is to be searched. Users can also specify the length of related terms and the relation between the search term and the related term. The suggestion system uses cross-searching to obtain related terms with the given length of word n-grams that are also consistent with both the user's search intent and the word meaning in the context of the given recipe documents. Two salient characteristics of the proposed method are (1) document structure recognition and (2) word n-gram length. Results of experiments conducted using standard text collection demonstrate that searches conducted with these two characteristics outperform baseline searches significantly. An exploratory search using query expansion with the proposed method is also described in this report.