This two-volume set, consisting of LNCS 6608 and LNCS 6609, constitutes the thoroughly refereed proceedings of the 12th International Conference on Computer Linguistics and Intelligent Processing, held in Tokyo, Japan, in February 2011. The 74 full papers, presented together with 4 invited papers, were carefully reviewed and selected from 298 submissions. The contents have been ordered according to the following topical sections: lexical resources; syntax and parsing; part-of-speech tagging and morphology; word sense disambiguation; semantics and discourse; opinion mining and sentiment detection; text generation; machine translation and multilingualism; information extraction and information retrieval; text categorization and classification; summarization and recognizing textual entailment; authoring aid, error correction, and style analysis; and speech recognition and generation.

The nature of memory for everyday events, and the contexts that can affect it, are controversial topics being investigated by researchers in cognitive, social, clinical, and developmental/lifespan psychology today. This book brings many of these researchers together in an attempt to unpack the contextual and processing variables that play a part in everyday memory, particularly for emotion-laden events. They discuss the mental structures and processes that operate in the formation of memory representations and their later retrieval and interpretation.

Most research on social movements has ignored the significance of emotions. This edited volume seeks to redress this oversight and introduces new research themes and tools to the field of emotions and social movements. Sociologists and political activists around the world will find this volume to be of great interest due to its wide-ranging approach and its unique emphasis on the role of emotion in protest, dissent and social movements.

The current trends in technology suggest that the next generation of services and devices allows smarter customization and automatic context recognition. Computers learn the behavior of the users and can offer them customized services depending on the context, location, and preferences. One of the most important challenges in human-machine interaction is the proper understanding of human emotions by machines and automated systems. In the recent years, the progress made in machine learning and pattern recognition led to the development of algorithms that are able to learn the detection and identification of human emotions from experience. These algorithms use different modalities such as image, speech, and physiological signals to analyze and learn human emotions. In many settings, the vocal information might be more available than other modalities due to widespread of voice sensors in phones, cars, and computer systems in general. In emotion analysis from speech, an audio utterance is represented by an ordered (in time) sequence of features or a multivariate time series. Typically, the sequence is further mapped into a global descriptor representative of the entire utterance/sequence. This descriptor is used for classification and analysis. In classic approaches, statistics are computed over the entire sequence and used as a global descriptor. This often results in the loss of temporal ordering from the original sequence. Emotion is a succession of acoustic events. By discarding the temporal ordering of these events in the mapping, the classic approaches cannot detect acoustic patterns that lead to a certain emotion. In this dissertation, we propose a novel feature mapping framework. The proposed framework maps temporally ordered sequence of acoustic features into data-driven global descriptors that integrate the temporal information from the original sequence. The framework contains three mapping algorithms. These algorithms integrate the temporal information implicitly and explicitly in the descriptor's representation. In the rst algorithm, the Temporal Averaging Algorithm, we average the data temporally using leaky integrators to produce a global descriptor that implicitly integrates the temporal information from the original sequence. In order to integrate the discrimination between classes in the mapping, we propose the Temporal Response Averaging Algorithm which combines the temporal averaging step of the previous algorithm and unsupervised learning to produce data driven temporal contextual descriptors. In the third algorithm, we use the topology preserving property of the Self-Organizing Maps and the continuous nature of speech to map a temporal sequence into an ordered trajectory representing the behavior over time of the input utterance on a 2-D map of emotions. The temporal information is integrated explicitly in the descriptor which makes it easier to monitor emotions in long speeches. The proposed mapping framework maps speech data of different length to the same equivalent representation which alleviates the problem of dealing with variable length temporal sequences. This is advantageous in real time setting where the size of the analysis window can be variable. Using the proposed feature mapping framework, we build a novel data-driven speech emotion detection and recognition system that indexes speech databases to facilitate the classification and retrieval of emotions. We test the proposed system using two datasets. The first corpus is acted. We showed that the proposed mapping framework outperforms the classic approaches while providing descriptors that are suitable for the analysis and visualization of humans' emotions in speech data. The second corpus is an authentic dataset. In this dissertation, we evaluate the performances of our system using a collection of debates. For that purpose, we propose a novel debate collection that is one of the first initiatives in the literature. We show that the proposed system is able to learn human emotions from debates.

First Published in 2012. Routledge is an imprint of Taylor & Francis, an informa company.

This important volume defines the state of the art in the field of emotion and memory by offering a blend of research review, unpublished findings, and theory on topics related to its study. As the first contemporary reference source in this area, it summarizes findings on implicit and explicit aspects of emotion and memory, addresses conceptual and methodological difficulties associated with different paradigms and current procedures, and presents broad theoretical perspectives to guide further research. This volume articulates the accomplishments of the field and the points of disagreement, and gives the brain, clinical, and cognitive sciences an invaluable resource for 21st-century researchers. Citing and analyzing the results of experiments as well as field and case studies, the chapters are organized around methodological approaches, biological-evolutionary perspectives, and clinical perspectives, and bring together experts in neuroscience, and both cognitive and clinical psychology. Questions addressed include: * What is the nature of emotional events and what do we retain from them? * Is there something about emotional events that causes them to be processed differently in memory? * Do emotional memories have special characteristics that differ from those produced by "ordinary" memory mechanisms or systems? * Do people with emotional disturbances remember differently than normal people? * Which factors play the most crucial role in functional amnesia?