Evaluation Of Word Embeddings From Large-Scale French Web Content
This work provides publicly available French word embeddings that enhance performance in NLP tasks for French language applications, though it is incremental as it adapts existing methods to a new language.
The paper tackles the lack of high-quality word embeddings for French by training new vectors on massive crawled data and evaluating them on analogy and real NLP tasks, showing significant performance improvements over existing and random embeddings.
Distributed word representations are popularly used in many tasks in natural language processing. Adding that pretrained word vectors on huge text corpus achieved high performance in many different NLP tasks. This paper introduces multiple high-quality word vectors for the French language where two of them are trained on massive crawled French data during this study and the others are trained on an already existing French corpus. We also evaluate the quality of our proposed word vectors and the existing French word vectors on the French word analogy task. In addition, we do the evaluation on multiple real NLP tasks that shows the important performance enhancement of the pre-trained word vectors compared to the existing and random ones. Finally, we created a demo web application to test and visualize the obtained word embeddings. The produced French word embeddings are available to the public, along with the finetuning code on the NLU tasks and the demo code.