The MIT Natural Language Processing Group focuses on developing large corpora for language understanding, including the OpenWebText corpus and the BookCorpus.
Common Crawl is a non-profit organization that provides a large corpus of web pages for language understanding research, with over 25 terabytes of data.
The Wikipedia Corpus is a large corpus of text from Wikipedia, providing a valuable resource for language understanding research, with over 50 million articles in many languages.
The Pile Corpus is a large-scale corpus of text from the web, books, and other sources, used for training language models, with over 800 GB of text data.
This survey paper reviews the current state of language understanding research using large corpora, including the challenges and opportunities of working with large datasets.
Google's large corpus for language understanding is used to train its language models, including BERT and its variants, with a massive dataset of text from the web and other sources.
The Stanford NLP Group uses large corpora for language understanding research, including the Stanford Question Answering Dataset (SQuAD) and the Stanford Sentiment Treebank.
This tutorial video provides an introduction to large corpora for language understanding, including how to work with popular datasets and tools, such as NLTK and spaCy.