A Study of a Non-Resourced Language: The Case of one of the Algerian Dialects

International audienceThis paper presents a linguistic study of an algerian arabic dialect, namely the dialect of Annaba (AD). It also presents the methodology applied in the construction of a parallel corpus MSA-AD. This work is done in a future goal of developing a machine translation system of standard Arabic (MSA) to algerian arabic dialects

Conflict Ontology Enrichment Based on Triggers

International audienceIn this paper, we propose an ontology-based approach that enables to detect the emergence of relational conflicts between persons that cooperate on computer supported projects. In order to detect these conflicts, we analyze, using this ontology, the e-mails exchanged between these people. Our method aims to inform project team leaders of such situation hence to help them in preventing serious disagreement between involved employees. The approach we present builds a domain ontology of relational conflicts in two phases. First we conceptualize the domain by hand, then we enrich the ontology by using the trigger model that enables to find out terms in corpora which correspond to different conflicts

Identifying Conflicts Through Emails by Using an Emotion Ontology

International audienceIn the logic of text classification, this paper presents an approach to detect emails conflict exchanged between colleagues, who belong to a geographically distributed enterprise. The idea is to inform a team leader of such situation, hence to help him in preventing serious disagreement between team members. This approach uses the vector space model with TF*IDF weight to represent email; and a domain ontology of relational conflicts to determine its categories. Our study also addresses the issue of building ontology, which is made up of two phases. First we conceptualize the domain by hand, then we enrich it by using the triggers model that enables to find out terms in corpora which correspond to different conflicts

How to handle gender and number agreement in statistical language models

Abstract The agreement in gender and number is a critical problem in statistical language modeling. One of the main difficulties in speech recognition of French language is the presence of misrecognized words due to the bad agreement (in gender and number) between words. Statistical language models do not treat this phenomena directly. This paper focuses on how to handle the issue of this agreement. We introduce an original model called Features-Cache (FC) to estimate the gender and the number of the word to predict. It is a dynamic variable-length Features-Cache. The size of the cache is automatically determined in accordance to syntagm delimitors. The main advantage of this model is that there is no need to any syntactic parsing : it is used as any other statistical language model. Several models have been carried out and the best one achieves an improvement of approximatively 9 points in terms of perplexity. This model has been integrated in a speech recognition system based on JULIUS engine. Tests have been carried out on 280 sentences provided by AUPELF for the French automatic speech recognition evaluation campaign. This new model outperforms the baseline one, in terms of word error, by 3%

An empirical study of the Algerian dialect of Social network

International audienceIn this paper, we present analysis on the use of Algerian dialect in Youtube. To do so, we harvested a corpus of 17M of words. This latter was exploited to extract a comparable Algerian corpus, named CALYOU by aligning pairs of sentences written in Latin and Arabic. This one was built by using a multilingual word embeddings approach. Several experiments have been conducted to fix the parameters of the Continuous Bag of Words approach that will be discussed in this article. The method we proposed achieved a performance of 41% in terms of Recall. In the following, we present several figures on the collected data that led to several unexpected results. In fact, 51% of the vocabulary words are written in Latin script and 82% of the total comments are subject to the phenomenon of code-switching

Vers une meilleure modélisation du langage : la prise en compte des séquences dans les modèles statistiques

Colloque avec actes et comité de lecture. nationale.National audienceNous trouvons dans la langue naturelle, plusieurs séquences de mots clés traduisant la structure d'une phrase. Ces séquences sont de longueur variable et permettent d'avoir une élocution naturelle. Pour tenir compte de ces séquences lors de la reconnaissance de la parole, nous les avons considérées comme des unités et nous les avons ajoutées au vocabulaire de base. Par conséquent, les modèles de langage utilisant ce nouveau vocabulaire se fondent sur un historique d'unités où chacune d'entre elles peut être, soit un mot, soit une séquence. Nous présentons dans ce papier une méthode originale d'extraction de séquences de mots linguistiquement viable ; cette méthode se fonde sur le principe de la théorie de l'information. Nous exposons également dans ce papier différents modèles de langage se basant sur ces séquences. l'évaluation a été effectué avec un dictionnaire de 20000 mots et avec un corpus de 43 million de mots. l'utilisation des séquences a amélioré la perplexité d'environ 23% et le taux d'erreur de notre système de reconnaissance vocale MAUD d'environ 20%. || In natural language, several sequences of words are very frequent. Conventional language models do not adequately take into account such sequences, because they underestimate their probabilities. A better approach consists in modeling word sequences as i

Investigating data sharing in speech recognition for an underresourced language: the case of algerian dialect

International audienceThe Arabic language has many varieties, including its standard form, Modern Standard Arabic (MSA), and its spoken forms, namely the dialects. Those dialects are representative examples of under-resourced languages for which automatic speech recognition is considered as an unresolved issue. To address this issue, we recorded several hours of spoken Algerian dialect and used them to train a baseline model. This model was boosted afterwards by taking advantage of other languages that impact this dialect by integrating their data in one large corpus and by investigating three approaches: multilingual training, multitask learning and transfer learning. The best performance was achieved using a limited and balanced amount of acoustic data from each additional language, as compared to the data size of the studied dialect. This approach led to an improvement of 3.8% in terms of word error rate in comparison to the baseline system trained only on the dialect data

LORIA System for the WMT13 Quality Estimation Shared Task

International audienceIn this paper we present the system we submitted to the WMT13 shared task on Quality Estimation. We participated to the Task 1.1. Each translated sentence is given a score between 0 and 1. The score is obtained by using several numerical or boolean features calculated according to the source and target sentences. We perform a linear regression of the feature space against scores in the range [0..1], to this end, we use a Support Vector Machine with 66 features. In this paper, we propose to increase the size of the training corpus. For that, we decide to use the post-edited and reference corpora in the training step after assigning a score to each sentence of these corpora. Then, we tune these scores on a development corpus. This leads to an improvement of 10.5% on the development corpus, in terms of Mean Average Error, but achieves only a sligth improvement on the test corpus

Fiabilité de la référence humaine dans la détection de thème

Colloque avec actes et comité de lecture. nationale.National audienceDans cet article, nous nous intéressons à la tâche de détection de thème dans le cadre de la reconnaissance automatique de la parole. La combinaison de plusieurs méthodes de détection montre ses limites, avec des performances de 93.1 %. Ces performances nous mènent à remettre en cause le thème de référence des paragraphes de notre corpus. Nous avons ainsi effectué une étude sur la fiabilité de ces références, en utilisant notamment les mesures Kappa et erreur de Bayes. Nous avons ainsi pu montrer que les étiquettes thématiques des paragraphes du corpus de test comportaient vraisemblablement des erreurs, les performances de détection de thème obtenues doivent donc êtres exploitées prudemment

Comparison of Topic Identification methods for Arabic Language

International audienceIn this paper we present two well-known methods for topic identification. The first one is a TFIDF classifier approach, and the second one is a based machine learning approach which is called Support Vector Machines (SVM). In our knowledge, we do not know several works on Arabic topic identification. So that we decide to investigate in this article. The corpus we used is extracted from the daily Arabic newspaper it Akhbar Al Khaleej, it includes 5120 news articles corresponding to 2.855.069 words covering four topics : sport, local news, international news and economy. According to our experiments, the results are encouraging both for SVM and TFIDF classifier, however we have noticed the superiority of the SVM classifier and its high capability to distinguish topics