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Section: New Results
Large-scale raw corpus development
Participants : Benoît Sagot, Éric Villemonte de La Clergerie, Laurent Romary, Pedro Ortiz Suárez, Murielle Fabre, Louis Martin, Benjamin Muller, Yoann Dupont.
In order to be in phase (and comparable) with the US partners of the “Petit-Prince” ANR project, Murielle Fabre assembled two French corpora:
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a small corpus for domain adaptation to children’s books: it will permit the fine tuning of the different parsers to a great amount of dialogues and Q&A present in Le Petit Prince.
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a large corpus of Contemporary French oral transcriptions and texts to calculate lexical association measures (AM) like PMI (Point-wise Mutual information) or Dice scores on the MWEs found in Le Petit Prince. This corpus of approx. 600 millions words, called CaBerNET, represents a balanced counterpart to the American COCA corpus. (https://corpus.byu.edu/coca/)
We have also developed a general, highly parallel, multi-threaded pipeline to clean and classify Common Crawl by language. Common Crawl is a huge (over 20TB), heterogeneous multilingual corpus comprised of documents crawled from the internet, not sorted per language. We designed our pipeline, called goclassy, so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We have created and we distribute a 6.3TB version of Common Crawl, called OSCAR, which is filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications [29]. OSCAR corpora served as input data to train a variety of neural language models, including the French BERT model CamemBERT (see relevant module for more information). Bridging corpus development, NLP and computational neurolinguistics on of our next step is to train BERT model with the above cited French balanced corpus CaBerNet to create CaBERTnet and extract form it parsing metrics that will be correlated with brain activity as measured by French fMRI recording while listening Le Petit Prince in French.