From d42289f15586189683ec7becaba4f8bbce09c5e4 Mon Sep 17 00:00:00 2001 From: muflax Date: Fri, 29 Jun 2012 12:42:22 +0200 Subject: [PATCH] typo --- content_daily/log/64.mkd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content_daily/log/64.mkd b/content_daily/log/64.mkd index cd1b039..19f323e 100644 --- a/content_daily/log/64.mkd +++ b/content_daily/log/64.mkd @@ -15,7 +15,7 @@ I implemented [James Tauber's next-best algorithm][graded-reader] for comparison However, I'm now unconvinced it's even all that more efficient. -For comparison, I'm running this against Le Petit Prince, which has ~2800 unique words and ~1600 sentences. Based on statistics from all other texts I've run this on, it's a completely typical text[^typ]. I assume almost no previous knowledge of the language. Also, we need some redundancy, so every new word must appear on at least 2 dedicated cards if it is unknown, or just 1 if it's already familiar (a related form has featured before). +For comparison, I'm running this against Le Petit Prince, which has ~2800 unique words and ~1600 sentences. Based on statistics from all other texts I've run this on, it's a completely typical text. I assume almost no previous knowledge of the language. Also, we need some redundancy, so every new word must appear on at least 2 dedicated cards if it is unknown, or just 1 if it's already familiar (a related form has featured before). Using the next-best algorithm, you'd cover all ~2800 words with ~3580 cards, giving you 1.29 cards per word. Only 87 sentences are not used.