#classifier

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When talking about measure word in Chinese, do you still only use 个? 

I don’t recommend that you use个(gè) as a universal measure word for two big reasons:  1) It makes you sound like a baby, and people will think you sound uneducated or unnecessarily cutesy.  2) If you take the time to learn the measure words, it will exercise your memory muscles and help you mentally organize your vocabulary into measure word categories. In the long run, this will allow you to retain a larger vocabulary. 

Neural networks are algorithms that learn by example, rather than by following a programmer’s set of rules. Although on this blog I’ve mostly been using them to generate new examples of things (like paint colors, halloween costumes, or craft beers), neural networks can do a lot more.

One thing neural networks can do is classify things. Give them a bunch of examples of one kind of thing, and a bunch of examples of another kind of thing, and it will (hopefully) learn to tell the two apart. This is really useful - for identifying obstacles for self-driving cars, for telling diseased tissue from healthy tissue, and even (with mixed success) for identifying spam or troll comments. I wanted to test this kind of algorithm out, so I devised the simplest task I could think of: telling metal bands from My Little Ponies.

I’ve previously trained text-generating algorithms to generate metal bands and My Little Ponies, so I had datasets ready to go. IBM Watson has a very easy-to-use tool for training classifiers (there’s a classroom-friendly version at machinelearningforkids.co.uk). I loaded in all 1,300 of the My Little Pony names I had, and filled the rest of the tool’s memory with metal bands (about 18,700). 

Then I entered some new pony names - neural network-generated pony names so they weren’t in the original dataset - to see how it would classify them. The result:

The neural network labeled *everything* as metal. People who have worked with neural network classifiers before will have seen this coming: with a dataset that was 94% metal class and only 6% pony class, I had set myself up with a classic case of something called class imbalance. The neural network found it could achieve 94% accuracy on my training dataset by calling everything metal. Princess Pie? Metal band with 81% confidence. Sweetie Loo? 85% likely to be metal. Sparkle Cheer? 84% sure that’s a metal band. Flutter Buns? So, so metal. 97%. The only names it didn’t label as metal bands were ponies that were the original dataset. So, Twilight Sparkle? 100% pony. Twilight Sprinkle, though? 83% metal.

The fix was easy: I trained the classifier again, this time with equal numbers of ponies and metal bands. This time the results were a lot more believable. And, the classifier network mostly agreed with the generator neural network names. There were some surprises, though.

Generated metal bands:

Dragonred of Blood - 100% metal, 0% pony Deathhouse - 97% metal, 3% pony Vermit - 97% metal, 3% pony Sespessstion Sanicilevus - 97% metal, 3% pony Stormgarden - 97% metal, 3% pony Vomberdean - 96% metal, 4% pony Swiil - 96% metal, 4% pony Dragorhast - 96% metal, 4% pony Sun Damage Omen - 96% metal, 4% pony Squeen - 96% metal, 4% pony Inhuman Sand - 88% metal, 12% pony Snapersten - 3% metal, 97% pony Staggabash - 3% metal, 97% pony

Generated ponies:

Cherry Curls - 0% metal, 100% pony Starly Star - 1% metal, 99% pony Cheese Breeze - 0% metal, 100% pony Agar Swirl - 1% metal, 99% pony Sob Dancer - 1% metal, 99% pony Derdy Star - 1% metal, 99% pony Princess Sweat - 1% metal, 99% pony Raspberry Turd - 1% metal, 99% pony Arple Robbler - 3% metal, 97% pony Pocky Mire - 6% metal, 94% pony Cold Sting - 10% metal, 90% pony Pearlicket - 48% metal, 52% pony Blue Cuss - 79% metal, 21% pony Sunsrot - 84% metal, 16% pony Rade Slime - 84% metal, 16% pony Flustershovel Aoetel Pakeecuand - 96% metal, 4% pony

When I fed the classifier names that were generated by a neural network trained on BOTH metal bands and ponies, it was not as confused as I had expected. Instead, it classified them with high confidence as one or the other.

Pinky Doom - 99% metal, 1% pony Strike Berry - 0% metal, 100% pony Cryptic Mane - 1% metal, 99% pony Bloody Star - 4% metal, 96% pony Killy Power - 96% metal, 4% pony Spectral Apple - 1% metal, 99% pony

Of course, this classifier will also work on any text I give it.

Benedict Cumberbatch - 96% metal, 4% pony Jane Austen - 17% metal, 83% pony Dora the Explorer - 55% metal, 45% pony Aluminum - 17% metal, 83% pony Aluminium - 96% metal, 4% pony The Earth’s Core - 99% metal, 1% pony Lobsters - 18% metal, 82% pony Opossums - 97% metal, 3% pony Yogurt - 17% metal, 83% pony Kumquats - 96% metal, 4% pony

According to this neural network, we may need to rethink Star Wars canon. Leia Organa - 96% metal, 4% pony Luke Skywalker - 31% metal, 69% pony Darth Vader - 19% metal, 81% pony Kylo Ren - 18% metal, 82% pony

I had some fun with the metal/pony mashup algorithm and generated more names than will fit here. Become a supporter of AI Weirdness and get them as bonus content!

Hello and thanks for asking :D

So classifiers (also referred to as “counters”). 

First of all, the structure : 

When you count things in Korea you don’t say “one cat”, “two cows”, “three books”.

You would say something that can be translated as : cat, one, classifier-for-animals, have/exist. ex : 고양이가 한 마리 있어요.

Maybe you can see the idea as the following : in English, you often use words like any, some, a piece of, a slice of, a bunch of, ... There are always use when counting. Well it’s the same with classifiers.

There are a lot of different counters but here is a list for you to start (in bold the ones you probably need to learn first) :

명 - to count people

개 - to count objects (when in doubt, better use this one)

잔 - to count glasses, cups, etc (ex : a cup of coffee = 커피 한잔 주세요.)

병 - to count bottles

장 - to count sheets, or paper-like material in general (not to be confused with 잔 ^-^)

권 - to count books

마리 - to count animals

송이 - to count picked flowers

그루 -  to count trees

살 - when giving the age

대 - to count machines and pieces

벌 - to count clothes

곡 - to count pieces of music

편 - to count movies, dramas,poems, musicals 

Important :  When you use counters, most of the time, they are used along with native Korean numbers (하나. 둘. 셋)

To make a long story short : when counting things, say 

- what you are counting (ex : cat),

- how many/much (ex : 30, yeah that’s a lot of cats!! xD),

- which class of things the noun belongs to (ex : animals)

And don’t forget the question that goes with this lesson : Noun 몇 counter 있어요? (ex: 책이 몇 권 있어요?)