Mohamed Morchid

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In this paper, we present the participation of the Computer Science Laboratory of Avignon (LIA) to RepLab 2013 edition. RepLab is an evaluation campaign for Online Reputation Management Systems. LIA has produced a important number of experiments for every tasks of the campaign: filtering, topic priority detection, Polarity for Reputation and topic(More)
In this paper, we present a method of tweet contextualization by using a semantic space to extend the tweet vocabulary. This method is evaluated on the tweet contextualization benchmark. Contextualization is build with the sentences from English Wikipedia. The context is obtained by querying a baseline system of summary. The query is made with words from a(More)
Various studies highlighted that topicbased approaches give a powerful spoken content representation of documents. Nonetheless, these documents may contain more than one main theme, and their automatic transcription inevitably contains errors. In this study, we propose an original and promising framework based on a compact representation of a textual(More)
In this paper, we describe the LIA system proposed for the MediaEval 2013 Spoken Web Search task. This multilanguage task involves searching for an audio content query, in a database, with no training resources available. The participants must then find locations of each given query term within a large database of untranscribed audio files. For this task,(More)
This article presents two methods for the automatic detection of social events that were evaluated on the annotated set of pictures as part of the 2011 Mediaeval benchmark [1]. The first method uses a set of web pages and a semantic space obtained by Latent Dirichelet Allocation (LDA, [2, 3]) to classify pictures from Flickr. The second approach uses the(More)
Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with respect to a sequence of inputs. Recurrent hidden cells allow these DNN-based models to manage long-term dependencies such as Recurrent Neural Networks (RNN) and Long Short-Term(More)
In this paper, we study the impact of dialogue representations and classification methods in the task of theme identification of telephone conversation services having highly imperfect automatic transcriptions. Two dialogue representations are firstly compared: the classical Term Frequency-Inverse Document Frequency with Gini purity criteria (TF-IDF-Gini)(More)
The paper introduces new features for describing possible focus variation in a human/human conversation. The application considered is a real-life telephone customer care service. The purpose is to hypothesize the dominant theme of conversations between a casual customer calling. Conversations are processed by an automatic speech recognition system that(More)
Although the current transcription systems could achieve high recognition performance, they still have a lot of difficulties to transcribe speech in very noisy environments. The transcription quality has a direct impact on classification tasks using text features. In this paper, we propose to identify themes of telephone conversation services with the(More)