This paper addresses the issue of comparability of comments extracted from Youtube. The comments concern spoken Algerian that could be either local Arabic, Modern Standard Arabic or French. This diversity of expression gives rise to a huge number of problems concerning the data processing. In this article, several methods of alignment will be proposed and tested. The method which permits to best align is Word2Vecbased approach that will be used iteratively. This recurrent call of Word2Vec allows us improve significantly the results of comparability. In fact, a dictionary-based approach leads to a Recall of 4, while our approach allows one to get a Recall of 33 at rank 1. Thanks to this approach, we built from Youtube CALYOU, a Comparable Corpus of the spoken Algerian.
A substantial majority of individuals who are exposed to a traumatic event do not develop any persistent trauma-related psychological symptoms, a phenomenon referred to as resilience. Relying on a sample of undergraduate students (n = 79), the present study investigated whether positive and negative affect and cognitive reactivity to emotional challenges serve as predictors of longitudinal changes in resilience as measured by the ConnorDavidson Resilience Scale. While at initial testing both positive affect and cognitive reactivity were related to resilience, only higher levels of cognitive reactivity predicted a reduction in resilience four months later. These results highlight the relevance of cognitive reactivity for the study of resilience.
In this paper, we present and evaluate a method for extractive textbased summarization of Arabic videos. The algorithm is proposed in the scope of the AMIS project that aims at helping a user to understand videos given in a foreign language (Arabic). For that, the project proposes several strategies to translate and summarize the videos. One of them consists in transcribing the Arabic videos, summarizing the transcriptions, and translating the summary. In this paper we describe the video corpus that was collected from YouTube and present and evaluate the transcription-summarization part of this strategy. Moreover, we present the Automatic Speech Recognition (ASR) system used to transcribe the videos, and show how we adapted this system to the Algerian dialect. Then, we describe how we automatically segment into sentences the sequence of words provided by the ASR system, and how we summarize the obtained sequence of sentences. We evaluate objectively and subjectively our approach. Results show that the ASR system performs well in terms of Word Error Rate on MSA, but needs to be adapted for dealing with Algerian dialect data. The subjective evaluation shows the same behaviour than ASR: transcriptions for videos containing dialectal data were better scored than videos containing only MSA data. However, summaries based on transcriptions are not as well rated, even when transcriptions are better rated. Last, the study shows that features, such as the lengths of transcriptions and summaries, and the subjective score of transcriptions, explain only 31% of the subjective score of summaries.
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