Gilbert Badaro

EURECOM · Data Science Department

In the last few years, the natural language processing community has witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in tabular data, recent research efforts extend LMs by developing neural representations for structured data. In this article, we pr...

In the last few years, the natural language processing community witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in relational tables, recent research efforts extend LMs by developing neural representations for tabular data. In this tutorial, we pre...

The High Luminosity LHC (HL-LHC) will start operating in 2027 after the third Long Shutdown (LS3), and is designed to provide an ultimate instantaneous luminosity of 7:5 × 10 ³⁴ cm ⁻² s ⁻¹ , at the price of extreme pileup of up to 200 interactions per crossing. The number of overlapping interactions in HL-LHC collisions, their density, and the resu...

Success of Natural Language Processing (NLP) models, just like all advanced machine learning models, rely heavily on large -scale lexical resources. For English, English WordNet (EWN) is a leading example of a large-scale resource that has enabled advances in Natural Language Understanding (NLU) tasks such as word sense disambiguation, question ans...

The Data Acquisition (DAQ) system of the Compact Muon Solenoid (CMS) experiment at the LHC is a complex system responsible for the data readout, event building and recording of accepted events. Its proper functioning plays a critical role in the data-taking efficiency of the CMS experiment. In order to ensure high availability and recover promptly...

The CMS experiment will be upgraded for operation at the HighLuminosity LHC to maintain and extend its physics performance under extreme pileup conditions. Upgrades will include an entirely new tracking system, supplemented by a track finder processor providing tracks at Level-1, as well as a high-granularity calorimeter in the endcap region. New f...

Opinion mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language which is the 5th most spoken language, and has b...

While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the...

With the advancement of Web 2.0, social networks experienced a great increase in the number of active users reaching 2 billion active users on Facebook at the end of 2017. Consequently, the size of text data on the Internet increased tremendously. This textual data is rich in knowledge, which attracted many data scientists as well as computational...

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textu...

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion...

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstan-dardized dialectal variations, the non-conformation to grammatical rules, the use of Arabizi and code-switching, and the...

Since human's emotions play a central role in everyday decisions and well-being, developing systems for recognizing and managing human's emotions captured significant research interest in the last decade. However, there is limited research on studying emotion recognition from human-computer interaction (HCI) in natural settings. This work aims at p...

In this paper, deep learning framework is proposed for text sentiment classification in Arabic. Four different architectures are explored. Three are based on Deep Belief Networks and Deep Auto Encoders, where the input data model is based on the ordinary Bag-of-Words, with features based on the recently developed Arabic Sentiment Lexicon in combina...

Most advanced mobile applications require server-based and communication. This often causes additional energy consumption on the already-limited mobile devices. In this work, we provide to address these limitations on the mobile for Opinion Mining in Arabic. Instead of relying on compute-intensive NLP processing, the method uses an Arabic lexicon r...

In this paper, we explore the effectiveness of deep learning models for text sentiment classification in Arabic. We propose the evaluation of Deep Belief Networks and deep Auto Encoders models. Three architectures are derived using the selected Arabic data set. The deep learning models are trained with features based on the recently developed Arabi...

Recommender systems provide recommendations on variety of personal activities or relevant items of interest. They can play a significant role for E-commerce and in daily personal decisions. However, existing recommender systems still face challenges in dealing with sparse data and still achieving high accuracy and reasonable performance. The issue...

Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address...

Recommender systems face performance challenges when dealing with sparse data. This paper addresses these challenges and proposes the use of Harmonic Analysis. The method provides a novel approach to the user-item matrix and extracts the interplay between users and items at multiple resolution levels. New affinity matrices are defined to measure si...

The proliferation of powerful smart devices is revolutionizing mobile computing systems. A particular set of applications that is gaining wide interest is recommender systems. Recommender systems provide their users with recommendations on variety of personal and relevant items or activities. They can play a significant role in today's life whether...