Associative classification is a novel and powerful method originating from association rule mining. In the previous studies, a relatively small number of high-quality association rules were used in the prediction. We propose a new approach in which a large number of association rules are generated. Then, the rules are filtered using a new method which is equivalent to a deterministic Boosting algorithm. Through this equivalence, our approach effectively adapts to large-scale classification tasks such as text categorization. Experiments with various text collections show that our method achieves one of the best prediction performance compared with the state-of-the-arts of this field.

This paper discusses development of a multi-domain conversational dialog system for simultaneously managing chats and goal-oriented dialogs. In this paper, we present a UMDM (Unified Multi-domain Dialog Manager) using a novel example-based dialog management technique. We have developed an effective utterance classifier with linguistic, semantic, and keyword features for domain switching and an example-based dialog modeling technique for domain-portable dialog models. Our experiments show that our approach is very useful and effective in multi-domain dialog system. Index Terms-dialog management, multi-domain dialog system, domain spotter, example-based dialog modeling

Abstract-In a real world, emotion plays a significant role in rational actions in human communication. Given the potential and importance of emotions, in recent years, there has been growing interest in the study of emotions to improve the capabilities of current human-robot interaction. The emotion recognition from text modality is a necessary step to develop affective conversational interfaces. In this paper, we present an effective hybrid approach to improve the performance of emotion recognition from text by combining linguistic, pragmatic, and keyword spotting features.

Spoken language understanding (SLU) addresses the problem of mapping natural language speech into semantic frame for structure encoding of its meaning. Most of the SLU systems separate out the dialog act (DA) identification from the named entity (NE) recognition to generate the semantic frames. In previous works, these two subtasks are treated by independent or cascaded approaches. In the cascaded systems, however, DA and NE influence only to one side, rather than to both sides. In this paper, we develop a new joint SLU model with a triangular-chain conditional random field (CRF) to encode inter-dependence between DA and NE. On four real dialog data, we show that our joint approach outperforms both independent and cascaded approaches.

Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error recovery is focused on the re-phrase strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.