Originally posted at hoelz.ro
I'm currently following the Fluent Forever methodology for learning Russian. The method suggests that when you start learning vocabulary, you should learn 625 common, concrete words that you can easily associate with pictures and feelings. The next step after this is to build your vocabulary to include the 1,000 most common words in your target language. I recently completed the foundation of 625 words, so it was time to move on to the next 1,000. There was just one small problem...
Russian, along with its Slavic brethren, has a distinction reflected in its verbs known as aspect (I touched upon this in a previous post). Nearly all verbs have a counterpart - one is perfective, which indicates that an action has been completed; the other is imperfective, which indicates that an action is in progress or happens habitually. For example (apologies if you speak Russian and I screwed up your language!):
Я ел сыр каждый день когда я жил в Амстердаме. - I ate cheese every day when I lived in Amsterdam. (using есть, imperfective "to eat")
Я съел гамбургер один час назад. - I ate a hamburger an hour ago. (using съесть, perfective "to eat")
So my problem was this:
- My starter list of 625 words didn't indicate which aspect the verb belonged to, nor did it include the counterpart for each verb.
- My list of the next 1,000 words contains verbs that may be the counterparts to some of the verbs already in my Anki deck. For example, I have говорить (to speak, imperfective) in deck; if the list of 1,000 has сказать, говорить's partner in crime, it would be odd to include it as a separate card.
So ideally, what I'd like to do is associate the verbs in my Anki deck with their counterparts, and prune any verbs I already know (along the their counterparts) from the "next 1,000" list.
To make this possible, I needed to find a mapping on perfective/imperfective counterparts in Russian. Fortunately for me, Wiktionary has a comprehensive data set on Russian verbs. A little web scraping magic and I was in business!
Don't Stress Out
The mapping I created based on the Wiktionary data looked like this:
говори́ть imperfective сказа́ть
If you look closely, you'll see an acute accent mark (◌́) over и and а. That's not really part of standard Russian orthography; you see, Russian's stress is unpredictable, so to make it possible for people like me to learn the language, or for native speakers to understand how to pronounce new words properly, the acute accent is often used in things like dictionaries to mark the syllable upon which stress falls. My Anki deck, however, doesn't have these markers - I have sound files to tell me which syllables to stress. So in order to compare the two sets of data, I needed to strip the marks first.
Since the marks were added via the
COMBINING ACUTE ACCENT character, the solution seemed to be pretty straightforward; it looked like this:
$verb .= subst(/<:Combining_Mark>/, '', :g);
Just strip all combining mark characters from the verb - seems simple, right?
But when I actually executed that code, I got the original string "говори́ть" back. What gives?
To understand why my code didn't work, it's helpful to understand Unicode normalization forms and how Perl 6 implements string operations. Normalization forms are a way of canonicalizing a Unicode string to make comparison and collation easier. To see how this works in practice, let's look at two strings: "á" and "á". Although they look the same, if you paste them into an application that can inspect Unicode (I like to just
echo "á" | xxd), you'll see that they're actually two different strings. The first is
LATIN SMALL LETTER A WITH ACUTE; the latter is
LATIN SMALL LETTER A +
COMBINING ACUTE ACCENT. In many programming languages, if you compare these two strings, they'll show up as unequal, because they are unequal when you think about them on a codepoint-by-codepoint basis. The solution to this is Unicode normalization, which allows us to translate a Unicode string to some canonical form which will compare equally. Under NFC normalization, for example, combining characters are condensed into precomposed forms if available, so both strings end up being just
LATIN SMALL LETTER A WITH ACUTE. Most programming languages require you to normalize your Unicode strings before doing any sort of comparison operation on them; Perl 6 is one of the few languages (Elixir is the only other language I know of where this is the case as well) that's an exception here:
say "\c[LATIN SMALL LETTER A WITH ACUTE]" eq "a\c[COMBINING ACUTE ACCENT]"; # True
The reason this "just works" in Perl 6 is because Unicode strings in Perl 6 are normalized into what's called NFG, or "Normal Form Grapheme". You can read Jonathan Worthington's explanations of NFG and its implementation here (PDF), here, and here, but in a nutshell, NFG means that each element of a string is a single character in the way humans think of characters - what we think of when we have to write a single character down on paper.
Think in Graphemes
My problem was that I was thinking on a codepoint level, rather than a grapheme level. Perl 6 looks at "и́" and friends as a single character - there are no combining marks anymore. You would run into this issue in other languages if you used NFC normalization (but not with Cyrillic characters, because they don't have precomposed forms with accents.). So the way I fixed this was to use the
Str.trans method, which maps one set of characters to another:
$verb .= trans('а́е́и́о́у́я́э́ы́ёю́' => 'аеиоуяэыёю');
Alternatively, I could've used the
Str.NFD method to get an NFD object back, and filtered out the
COMBINING ACUTE ACCENT codepoint before converting it back to a
Str. That felt too low-level to me - it would be nice if the
NFD and related types supported
Str-like operations such as
subst in the future.
Another alternative, suggested to me by teatime on IRC, would be to use the
$verb .= subst(rx:codes/<:Combining_Mark>/, '', :g);
...which tells the regex engine to think in terms of codepoints, not graphemes. Unfortunately, this has yet to be implemented.
After I made the change, I got the result I wanted, and I ended up with a beautiful list of words to add to my Anki deck. Success!
I hope that reading this educated you about Unicode a little bit, and will hopefully save you some time working with Unicode in the future, whether it's in Perl 6 or your favorite language!