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This paper conducts a thorough review of various mechanisms for calculating word embeddings and evaluates popular toolkits and publicly available embedding matrices. By experimenting with selected implementations, the authors provide insights into the characteristics and performance of these embeddings across different applications in Natural Language Processing. The findings highlight the strengths and limitations of current word embedding techniques, informing future research and practical applications in the field.
Word embeddings can significantly vary in performance, and understanding their characteristics is crucial for optimizing applications in NLP.
The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the recent past. Such embeddings have found application in areas such as Automatic Speech Recognition, Machine Translation, Sentiment Analysis and many more. This essay reviews the various mechanisms that have been proposed for the calculation of word embeddings, investigates popular toolkits and embedding matrices that are available in the public domain, and experiments with one or more selected implementations to better understand their characteristics. La repr\'esentation vectorielle continue de mots a \'et\'e l'un des d\'eveloppements les plus importants dans le domaine du traitement automatique du langage naturel au cours des derni\`eres ann\'ees. Ces repr\'esentations ont trouv\'e application dans des domaines tels que la reconnaissance vocale, la traduction automatique, l'analyse des sentiments, etc. Ce travail passe en revue les diff\'erents m\'ecanismes propos\'es pour le calcul de ces vecteurs de mots, \'etudie les kits d'outils populaires et les matrices disponibles publiquement en ligne, et exp\'erimente avec une ou plusieurs impl\'ementations s\'electionn\'ees pour mieux comprendre leurs caract\'eristiques.