Ze-Jing Chuang

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This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are(More)
This paper presents an emotion recognition system with textual input. In this system, an emotional semantic network is proposed to extract the semantic information related to emotion. The semantic network is composed of two subnetworks: a static semantic network and a dynamic semantic network. The static semantic network is established from an existing(More)
This paper describes our work at the sixth NTCIR workshop on the subtask of C-C single language information retrieval. We compared label propagation (LP), K-nearest neighboring (KNN), and relevance feedback (RF) for document re-ranking and found that RF is a more robust technique for performance improvement, while LP and KNN are sensitive to the choice and(More)
This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional(More)
This paper proposes two approaches to extract translation term pairs from Chinese-English bilingual corpus with more than 500,000 patents. One approach is precision-oriented, in which we compare six term alignment methods. Based on our experiments, we find that the EM (Expectation Maximization) method is the best. However, it is time-consuming and hard to(More)
This paper describes our experiment methods and results in the NTCIR-7 Patent Translation Task [1]. As the first step of our research in machine translation, we integrated a series of open source software to build a statistical translation model. The experiment results demonstrated that we still need to improve the performance and efficiency in both model(More)