The Beijing Language and Culture University created a balanced corpus of 15 billion characters. It’s based on news (人民日报 1946-2018，人民日报海外版 2000-2018), literature (books by 472 authors, including a significant portion of non-Chinese writers), non-fiction books, blog and weibo entries as well as...
Word frequency list based on a 15 billion character corpus: BCC (BLCU ...
I guess in my case, I could go with per-corpus flashcard sets to keep the per-corpus tagging, and one user dictionary (without tags) with all the per-corpus ranking info included in one entry per term.
Hello Mike, it occurred to me that it may be worthwhile to add an indicator for the frequency of a word in the upper right corner of a dictionary definition using the frequency data in the BCC corpus, allowing the user to see at a glance how common a word is. The frequency information could be...
The corpus is much larger than the CCL (470 million characters), the CNC (100 million characters), the SUBTLEX-CH (47 million characters) and the LCMC (less than 2 million characters). It seems as if the frequency lists derived from this corpus might be the most reliable frequency lists currently available.
With a small corpus of 650 articles from People's Daily, downloaded using a Python script, I hope to start providing a more modern frequency list of media-related vocabulary. The frequency list has the following features: It uses all sections of the 人民日报 / People's Daily newspaper, including the sports section.
PyCantonese comes with one built-in corpus, the Hong Kong Cantonese Corpus. For corpora other than HKCanCor, PyCantonese provides the function read_chat () to read in Cantonese data in the CHAT format. Someone with more skills than me could try to read 裏 through this python search from other corpuses and see what is the result.