Recognition and translation Arabic-French of Named Entities: case of the Sport places

The recognition of Arabic Named Entities (NE) is a problem in different domains of Natural Language Processing (NLP) like automatic translation. Indeed, NE translation allows the access to multilingual in-formation. This translation doesn't always lead to expected result especially when NE contains a person name. For this reason and in order to ameliorate translation, we can transliterate some part of NE. In this context, we propose a method that integrates translation and transliteration together. We used the linguis-tic NooJ platform that is based on local grammars and transducers. In this paper, we focus on sport domain. We will firstly suggest a refinement of the typological model presented at the MUC Conferences we will describe the integration of an Arabic transliteration module into translation system. Finally, we will detail our method and give the results of the evaluation

Multilingual Extraction of functional relations between Arabic Named Entities using NooJ platform

10 pagesInternational audienceThe extraction of relation between Named Entities (NE) has become the last few years an interesting research domain. It is very useful for many applications such as Web mining, Information extraction and retrieval, Business intelligence, Automatic databases filing with Entities & types, Questions answering task and document Summarization. Several works has been performed for relation discovery in texts written in Latin languages and as far as we know, very few works has been done for Arabic language. In this paper, we focus on functional relations between ENAMEX and ORG Arabic Named Entities. The extraction approach is rule based and the implementation is performed using NooJ Platform

Phrase-Based Language Model in Statistical Machine Translation

La date de publication ne nous a pas encore été communiquéeInternational audienceAs one of the most important modules in statistical machine translation (SMT), language model measures whether one translation hypothesis is more grammatically correct than other hypotheses. Currently the state-of-the-art SMT systems use standard word n-gram models, whereas the translation model is phrase-based. In this paper, the idea is to use a phrase-based language model. For that, target portion of the translation table are retrieved and used to rewrite the training corpus and to calculate a phrase n-gram language model. In this work, weperform experiments with two language models word-based (WBLM) and phrase-based (PBLM). The different SMT are trained with threeoptimization algorithms MERT, MIRA and PRO. Thus, the PBLM systems are compared to the baseline system in terms of BLUE and TER.The experimental results show that the use of a phrase-based language model in SMT can improve results and is especially able to reduce theerror rate

Grouping Like-Minded Users for Ratings’ Prediction

International audienceRegarding the huge amount of products, sites, information, etc., finding the appropriate need of a user is a very important task. Recommendation Systems (RS) guide users in a personalized way to objects of interest within a large space of possible options. This paper presents an algorithm for recommending movies. We break the recommendation task into two steps: (1) Grouping Like-Minded users, and (2) create model for each group to predict user-movie ratings. In the first step we use the Principal Component Analysis to retrieve latent groups of similar users. In the second step, we employ three different regression algorithms to build models and predict ratings. We evaluate our results against the SVD++ algorithm and validate the results by employing the MAE and RMSE measures. The obtained results show that the algorithm presented gives an improvement in the MAE and the RMSE of about 0.42 and 0.5201 respectively

Like-tasted user groups to predict ratings in recommender systems

International audienceRecommendation Systems have gained the intention of many researchers due to the growth of the business of personalizing, sorting and suggesting products to customers. Most of rating prediction in recommendation systems are based on customer preferences or on the historical behavior of similar customers. The similarity between customers is generally measured by the number of times customers liked or disliked the same item. Given the huge number and the variety of items, many customers cannot be considered as similar, as they did not evaluate the same items, even if they have similar tastes. This paper presents a new method of rating prediction in recommendation systems. The proposed method starts by identifying the taste directions or the interest centers based on the users' demographic information combined with their previous evaluations. Thus, it uses the Principal Component Analysis (PCA) to retrieve the major taste orientations. According to these orientations, user groups are created. Then, for each group, it generates a prediction model, that will be used to predict unknown rates of users within the corresponding group. In order to assess the accuracy of the proposed method, we compare its results with four baseline methods, namely: RegSVD, BiasedMF, SVD++ and MudRecS. Results prove that the proposed algorithm is more accurate than the base-line algorithms