Temporal Information Processing: A Survey

Temporal Information Processing is a subfield of Natural Language Processing, valuable in many tasks like Question Answering and Summarization. Temporal Information Processing is broadened, ranging from classical theories of time and language to current computational approaches for Temporal Information Extraction. This later trend consists on the automatic extraction of events and temporal expressions. Such issues have attracted great attention especially with the development of annotated corpora and annotations schemes mainly TimeBank and TimeML. In this paper, we give a survey of Temporal Information Extraction from Natural Language texts

Mining multimodal sequential patterns : a case study on affect detection

Temporal data from multimodal interaction such as speech and bio-signals cannot be easily analysed without a preprocessing phase through which some key characteristics of the signals are extracted. Typically, standard statistical signal features such as average values are calculated prior to the analysis and, subsequently, are presented either to a multimodal fusion mechanism or a computational model of the interaction. This paper proposes a feature extraction methodology which is based on frequent sequence mining within and across multiple modalities of user input. The proposed method is applied for the fusion of physiological signals and gameplay information in a game survey dataset. The obtained sequences are analysed and used as predictors of user affect resulting in computational models of equal or higher accuracy compared to the models built on standard statistical features.peer-reviewe