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Recently, I created a set of flashcards of single Chinese characters, to practice writing. The front of my Anki cards contained the pinyin, definition, and clozes for the most common words containing the character, while the back of the card was simply the character. I tagged the cards in groups of 200 by frequency rank, using tags of “1-200”, “201-400”, etc. I already know a number of characters, so I decided to start practicing with the more infrequent characters, the “1601-1800” tag.

There were some characters I was well familiar with. Other characters took more time to remember how to write, but weren’t too difficult, as I knew the characters on sight from extensive reading. But every once in a while I would be shown a card, and it would be for a character I had never seen before in 6 years! Some like 贼 (zéi, thief) or 鹏 (péng a mythical bird) were surprising to see in the 1600-1800 range for frequency ranks, ranked more frequent in the Lancaster Corpus than 垂 (chuí to hang down) and 夹 (jiā to squeeze). But however unusual they were, I still recall encountering them at some point (金色飞贼 is the golden snitch in Quidditch from Harry Potter, and 鹏 was from reading on Chinese mythical animals). However, 琉 (liú glazed tile) and 鲍 (bào abalone) don’t look familiar at all, and I am fairly certain I have never seen the characters 萼 (è calyx of a plant) and 懋 (mào diligent) in over 6 years of study. Is it just a strange chance that I haven’t encountered them, is it failing memory, or are they more rare than their frequency would suggest?

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In my Chinese studies, the Lancaster Corpus of Mandarin Chinese (LCMC) has been a useful source of data—word and character frequencies, collocations, phrase usage, parts of speech, etc. The corpus is freely available for non-commercial and research use. However, the native form of its data is in a set of XML files, which is not an easy format to work with. In addition, the XML data is slow to read data from, because all those XML tags and the entire data structure needs to be parsed. A much better format for the data is an SQL database. Stored in a database, many kinds queries and reports can be executed very efficiently. Depending on the software, these queries and reports can return results very quickly, much faster than in the XML format.

I have made available a Perl script and some other related tools to assist with extracting the LCMC files into a SQLite database. SQLite is a lightweight relational database management system intended for portability and ease of use. Because it functions as a standalone program (not client-server), it is easy to install and use. It’s more ubiquitous than you might think. It’s how the Firefox and Chrome browsers stores its history, cookies, and preferences. But it’s also used, for example, by the Anki program as the storage format for flashcard data, and by the Calibre e-reader program to store information on installed e-books.

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Knowing the frequency of the Chinese words you are studying helpful in a few different ways. If an unknown word is relatively common, then it’s generally more important to learn that word, compared to a less common word. With that knowledge in hand, you can feel less guilty about removing the rare words from your flashcards, and persist in learning the ones that are common yet difficult. If a word has a low frequency in general, but happens to be used a lot in a particular text, that word may be of interest to study. In the early stages of learning, studying the top N (100, 200, 500, etc.) words as flashcards is an effective way to bootstrap one’s word knowledge before diving into authentic texts. But it’s not 100% effective; the long tail of infrequent words will keep you busy learning new vocabulary for years!

So, how can we obtain word frequency data? With Chinese, it’s trickier than it sounds. › Continue reading…