Vocabulary makeover, please

Zipf’s Law: The frequency of a word is related exponentially to its rank in a frequency-ordered list. Practically speaking, this means that an adult studying a second language will run across words that they don’t know every day of their life.

To paraphrase Newton: if I speak better French than other Americans, it is only because I spend more time memorizing vocabulary.  My daily, daily, daily morning ritual: with my first cigarette and cup of coffee, I memorize 10 new words.  Zipf’s Law being what it is, I don’t exactly have to go hunting for words that I don’t know—over the course of the day, I note down every new word that I come across, and the next morning, I pick 10 of them to cram into the small amount of remaining space in my much-abused brain.

My go-to dictionaries are WordReference.com, followed by the Farlex French dictionary app.  If I’m pretty sure that I need more context, I go to the Sketch Engine web site if I have Internet access, and Linguee if I don’t.  Pretty straightforward, my little routine.  Quotidian.  Mundane.

Every once in a while, though, it does not yield the desired result.  Case in point: capillotracté.  Not in Word Reference, not in Farlex French.  So: Google… which gets me definitions that I don’t understand, because they make reference to an expression that I don’t understand: tirer quelqu’un par les cheveux.  And so, dear Readers: can you help an amerloque out?


My odyssey started in a place where you don’t expect to see casual use of language: Le Figaro.  The Fig’ is one of the Big 3 French newspapers, along with Libération (left) and Le Monde (center).  As you have probably guessed, Le Figaro is to the right of center.  Like many conservative people, it gets excited about prescribing language usage.  I don’t get excited about prescribing language usage, but I do get excited about language, so although I subscribe to Le Monde (I’m a lefty myself, but I figure that I’ll get the most representative sample of vocabulary more towards the center)I will often go to the Fig’ to read its language articles.  As you might expect from prescriptivists, they tend to be…precise.  Clear.  Unambiguous.  (Si ce n’est pas clair, ce n’est pas français, right?  Harumph.).

So, I’m reading an article on the subject of how to refer to Line 1 of the Paris metro—ligne un, or ligne une?—when I come across a word that I don’t know. I promptly copy it, along with the context in which I saw it, onto an index card (something that does not exist in France–see this post on the mystery):

The next morning, I go to look it up–and find nothing. Word Reference: no love. The Farlex French dictionary app: nope. Fine–I go to Google. I find definitions there, but they all refer to an expression whose meaning is opaque to me: tirer quelqu’un par les cheveux. For example:

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How about it, native speakers?  Can you help an amerloque out?  I’d pull my hair out over this, but I’m already bald…

The rule dit capillotracté?  Ligne un, because it’s a number, not the indefinite article.  The indefinite article un/une is inflected for gender, but the number un is not.


French notes

l’amerloque: American, person or language; noun or adjective. Familier et péjoratif.  Wiktionnaire alleges that it comes from Amérique plus oque, providing no evidence; I therefore claim equal plausibility for my own little theory, which is that it comes from Amérique plus locuteur.  Examples from Wiktionnaire, from which I stole them quite gleefully ’cause I don’t like their etymology:

  • […] mais c’est pas un spectacle pour une dame, rigola le jeunot à la casquette amerloque. — (Léo MaletLes rats de Montsouris, 1955)
  • Nom de Dieu, quand est-ce que tu vas arrêter de parler l’amerloque ? — (Sébastien Monod, Rue des Deux Anges, 2005)

English notes

makeover: “An overall treatment to improve something or make something more attractive or appealing.” (Source: American Heritage® Dictionary of the English Language, Fifth Edition. (2011). Retrieved September 11 2018 from https://www.thefreedictionary.com/makeover.)  There is an enormous quantity of makeover-themed TV shows.  Don’t judge me.

consult (noun): As a noun, this is stressed on the first syllable: CONsult.  consult is when you send someone or something to an expert, typically in a medical context.  For example, if you go to your doctor and they are pretty sure that you are having a neurological problem, they might tell their clerk to set you up with a neurology consult.  

In coming up with a title for this article, I thought about Vocabulary consult versus Vocabulary makeover.  The former would make a hell of a lot more sense, but since the word that I’m asking you to help me with has something to do with hair, I went for Vocabulary makeover.  Don’t like my choice?  Write your own fucking blog on the implications of the statistical properties of language for second-language learners.

to pull one’s hair out (over something): to have reached the point of frustration with a problem and still be unable to solve it.  Examples:

Bill, can you help me?  I’m pulling my hair out here… Every time I call the constructor, I get a “String Index Out Of Bounds” error, which makes no sense to me whatsoever…   

Dude, I’m pulling out my hair out over this budget. Every time I try to include the annual COL increase for salaries, the spreadsheet doubles the amount allotted for travel to the American Medical Informatics Association annual meeting. What the FUCK?? 

How I used it in the post: How about it, native speakers?  Can you help an amerloque out?  I’d pull my hair out over this, but I’m already bald…

Marde…and Kraft Dinner

I don’t even wanna think about what’s in that orange powder, but the stuff is strangely tasty.

Being the North American that I am, you would think that my French would be sprinkled with Canadianisms.  Not really: there are some words that I learned from Québécois and can’t seem not to pronounce like them–poussiaire when I should be saying poussière, lampadaillere when I should be saying lampadaire, and drette when I should be saying…well, actually I don’t know how to say drette in hexagonal French, which is why I say it in Québécois.  Some little stuff like that, but otherwise, you wouldn’t take me for a Canadian–ever.  (Well, there was this one incident on the métro… another time, perhaps.)


One exception to the general non-Canadianness of my (feeble) French: marde.  As an expletive, merde in Québec is…marde.  Why?  No clue.  Why is it what comes out of my mouth if I spill my coffee, drop my vocabulary flashcards on the RER B, or notice that I left my laundry in the washing machine overnight and now they’re moldy as fuck?  Also no clue.  But, if you wanna hear marde straight outta (outta explained in the English notes below) the mouth of an autochtone, you won’t find anything better than a recording of Québécoise superstar Lisa Leblanc.  She has a delightful accent–I believe from Newfoundland, given her pronunciation of words like gars as “guh.”  There are approximately one bazillion YouTube videos of her singing this song; I like this one because of her backup singers.  Linguistic mystery: why connes and not cons in

A matin mon lit simple fait sûr de me rappeler que je dors dans un lit simple avec les springs qui m’enfoncent dans le dos // Comme des connes…

…or maybe I’m just hearing it wrong?  Phil dAnge?  In any case: enjoy Lisa LeBlanc’s Ma vie c’est de la marde, and then scroll down to the English notes for a discussion of outta, plus a special bonus explanation of Kraft Dinner.  Why?  Keep reading, keep reading…

 


English notes

outta: an informal spoken form of “out of.”  Click here for a good video about how to use it.  It’s not typically written, but if it is, it’ll be o-u-t-t-a.  

Kraft Dinner: a disgusting but completely delicious kind of macaroni and cheese.  You buy it in a box, boil the pasta, sprinkle an envelope of orange powder on it, throw in some butter and some milk… I don’t even wanna think about what’s in that orange powder, but the stuff is strangely tasty, and at 25 cents a box the last time I checked (which was probably the last time that I could only afford 25 cents for dinner), you can live on it for surprisingly long.  Why it’s relevant to us today: it’s the title of a truly lovely Lisa LeBlanc song.

Au pire on vivra ensemble // En mangeant du Kraft Dinner // C’est tout ce qu’on a besoin…

 

Want to learn to speak Québécois?  Free lessons hereHilarious, and actually pretty helpful…

Amazing two-headed baby

What would a linguist say about it? Pretty much nothing.

Getting divorced mostly sucks (speaking from experience here–I do it a lot), but it does have one good side: you clean your basement.  Picking through old files from my days of teaching Linguistics 101, I found this old photo from the cover of the National Enquirer, a tabloid that you flip through while waiting in line at the grocery store and then occasionally buy despite yourself.

I found the headline interesting because it touches on a couple of recurrent themes in the history of thought about language, but goes in an unusual direction with it.  The themes:

  • The original language
  • Language deprivation experiments

The original language

There is a very long history of wondering what the original language was.  The top candidate in the various and sundry ravings about this is Hebrew.  Why?  It’s the language of the Bible (specifically, the Old Testament to those of you who are Christianically inclined).  Latin often comes up, too.

What would a linguist say about the question?  Pretty much nothing.  From the Hominidés.org web site:

Depuis le 17e siècle la question se posait : depuis quand l’homme utilisait-il le langage articulé ? De nombreuses théories ont été avancées dont certaines très farfelues (voir ci-contre). En 1866 la Société de Linguistique de Paris (fondée en 1864) mit un coup d’arrêt à ces tentatives fantaisistes et interdit tout simplement la publication de textes relatifs à l’origine du langage.

My translation: Since the 17th century, the question has been asked: from when have humans used spoken language?  Numerous theories have been advanced, some of which are quite nutty (or even French French French [too lazy to look up ci-contre on a Saturday morning]).  In 1866 the Linguistic Society of Paris (founded in 1864) French French French [see preceding bracketed statement] and completely forbad (forbade?) the publication of papers on the origin of language.

Why forbid study of the origin of language?  Because your theories are not testable, and if something is not at least in theory testable, it’s not science.  Linguists are not even certain that language originated just once–one explanation that has been advanced for the astounding variability in human languages is the polygenesis hypothesis, which proposes that language originated multiple times in different human(-ish) populations.  (The single-origin hypothesis is the monogenesis hypothesis.)  Hell, we’re not even certain that language originated in spoken form–it could well have been signed.  (Yes: signed languages are languages, like any other.)

 

Language deprivation experiments

The idea behind a language deprivation experiment is to deny children exposure to language and see what happens.  I’m not totally convinced that any of the reported language deprivation experiments (see some listed on this Wikipedia page) actually ever happened, but their stated motivations frequently include the belief that children who are not exposed to any language would spontaneously speak “the original language,” and guess what?  Latin is often reported as one of the anticipated tongues.

Language deprivation tragedies

In fact there is a depressing number of cases in which children actually have been deprived of exposure to language, either through mishap or through horrific criminal misdeeds.  What doesn’t happen when they’re rescued: they don’t speak Hebrew; neither do they speak Latin.  They don’t speak anything, and if they’re rescued too late, they never do.  (This is often taken as evidence supporting the critical period hypothesis about child language acquisition.)

The weird direction in which the National Enquirer takes their story

…is that they talk not about children who are old enough to have acquired language, but rather babies; they then take the kid-speaks-Latin phenomenon as a way to talk about proof of reincarnation.  Not unheard of (see here and here, and here), but not run-of-the-mill, either.


There’s that part of me that wants to talk about the role of two-headed babies in the history of genetics, but my breakfast ice cream is melting, so we’ll have to wait for another time…  Breakfast ice cream–yum…


English and French notes:

despite oneself: En dépit de soi-même, I think.  …tous mes efforts sont vains, je t’adore en dépit de moi-même.  (Jean-Jacques Rousseau, Julie ou la nouvelle Héloïse, which I am reading at the moment and find hilarious.)

to suck: a borderline vulgar way of saying to be bad in the sense of undesirable.  I ran across craindre un max as a French-language equivalent once, but nobody seems to recognize that when I say it.

to forbid: a super-irregular verb.  In French: interdire, I think.  (Man, I am really lazy today…)  From the bab.la web site (and I don’t buy forbid as a past participle at all, although once again, I’m too lazy today to look for actual evidence):

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Source: bab.la web site

 

 

 

Cabinet of Curiosities: Buying non-touristy stuff in Paris

The most common questions that people ask me about life in Paris:

  1. How come nobody in Paris speaks English?  (How come explained in the English notes below.)
  2. How come whenever I try to speak to people in Paris in French, they always answer me in English?
  3. Aren’t you afraid of terrorist attacks?
  4. Where can I buy non-touristy souvenirs?

(1) and (2) are, of course, contradictory, and I’ve written about them before (and will again, ’cause it’s super-complicated).  I’ve written about (3), too, and no, I’m not–every 3 days in the US, we have more gunfire deaths than Paris had in its worst terrorist attack in history.  I literally have a greater chance of being shot to death in a road rage incident on my way to work in the US than I do of dying in a terrorist attack in Paris.  Seriously.

(4): a question that I love to answer.  Today I’ll tell you where to buy non-touristy souvenirs in Montmartre.


Before there were museums, there was the cabinet of curiosities–le cabinet de curiosités.  If you were powerful, or maybe just really rich, your cabinet of curiosities was where you showed off your collection of … interesting stuff.  Mostly stuff from the natural world.  A narwhal’s tusk, say; rare stones; perhaps some fossils.  Showing it off was the point.  As Wikipedia puts it:

The Kunstkammer (cabinet of curiosities) of Rudolf II, Holy Roman Emperor (ruled 1576–1612), housed in the Hradschin at Prague, was unrivalled north of the Alps; it provided a solace and retreat for contemplation[3] that also served to demonstrate his imperial magnificence and power in symbolic arrangement of their display, ceremoniously presented to visiting diplomats and magnates.[4]

Montmartre is a neighborhood in the northern part of Paris.  As you might expect from the name Montmartre, it has an elevation, and at the peak of that elevation is one of Paris’s most popular tourist attractions: Sacré Coeur, “Sacred Heart,” France’s way of saying it’s sorry that Paris seceded from it in 1871.

I jest–bitterly: Sacré Coeur expresses France’s wish that Paris would say that it’s sorry that it seceded in 1871.  Sacré Coeur is reactionary France’s way of putting words in Paris’s mouth–specifically, an apology for having seceded from France in 1871.  As if it weren’t enough that the Versaillais (the soldiers of the national government) killed 20,000-ish Parisians when they retook the city.  La semaine sanglante, it’s called–The Bloody Week.


img_5991Descending from the aforementioned elevation on a Sunday-afternoon walk the other day, I came across Grégory Jacob and a truly delightful place to buy non-touristy stuff in Montmartre.  Curiositas is a charming little store in the style of a 19th-century cabinet of curiosities, complete with a nice selection of marlin snouts–far more practical in a little Parisian apartment than a narwhal tusk, and just as pointed.

Grégory spent 20 years as an optician before the insurance companies sucked the joy out of the profession, at which point he decided to become a boutiquier (see the French notes below for some subtleties of the terminology of shop-owners) and opened Curiositas.  His new profession lets him pursue his passions–la chine, la brocante, les curiosités, l’ostéologie, l’entomologie–in the very neighborhood where Gabriel loses his glasses and delivers his monologue in Zazie dans le métro.  

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Picture source: http://www.curiositas.paris

And all of those passions are represented–the wares on offer include skulls, bugs, and the super-cool apparatus for drinking absinthe.  (Who knew that there are nifty devices for holding the sugar cube over which you pour la fée verte, “the green fairy”–absinthe itself.  Hell, I didn’t even know that you pour it over a sugar cube.  Hell, again: I didn’t even know that they still make the stuff.)  You need coasters with anatomical organs on them?  Grégory’s got them.  An emu egg?  No problem.  Skulls?  Curiositas has both carnivores and herbivores.  You’re tired of the Montmartre crêpe shops, wannabe artists, and fabric stores?  Step into Curiositas.  Tell Grégory the weird American guy says hi.  Scroll down past the pictures for the English and French notes.

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How to prepare absinthe. Picture source: By Eric Litton – Own work, CC BY-SA 2.5, https://commons.wikimedia.org/w/index.php?curid=845302


English notes 

how come: an informal way of saying why.  Examples:

 

 

 

“Trex” is “T-rex,” Tyrannosaurus rex. The dinosaur with enormous fangs and tiny little arms.


French notes

le boutiquier :  shopkeeper.

le commerçant : shopkeeper, retailer.

le magasinier : l’employé qui s’occupe d’entreposer, ranger des marchandises dans un entrepôt. (Definition courtesy of Grégory)

Which TWD character is your classifier? Bias and variance in machine learning

You don’t expect the zombie apocalypse to be relevant to research in computational linguistics–and yet it is; it so, so is.

Spoiler alert: this post about the TV show The Walking Dead–which, I will note, is as popular in France as it is in the US–will tell you what happens to Carol around Season 3 or 4.

In general, it’s the stuff that surprises you that’s interesting, right?  No one ever expects the arctic ground squirrel to have anything to do with computational linguistics–and yet it does: it so, so does.  No one ever expects to be confronted with problems with the relationship between compositionality and the mapping problem over breakfast in a low-rent pancake house–and yet it happens; it so, so happens(Low-rent as an adjective explained in the English notes below.) You don’t expect the zombie apocalypse to be relevant to research in computational linguistics–and yet it is; it so, so is.


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You probably think that I just make this stuff up. I don’t! Picture source: https://www.slideshare.net/JenAman/large-scale-deep-learning-with-tensorflow

You’ve probably heard of machine learning.  It’s the science/art/tomfoolery of creating computer programs to learn things.  We’re not talking about The Terminator just yet–some of the things that are being done with machine learning, particularly developing self-driving cars, are pretty amazing, but mostly it’s about teaching computers to make choices.  You have a photograph, and you want to know whether or not it’s a picture of a cat–a simple yes/no choice.  You have a prepositional phrase, and you want to know whether it modifies a verb (I saw the man with a telescope–you have a telescope, and using it, you saw some guy) or a noun (I saw the man with a telescope–there is a guy who has a telescope, and you saw him).  Again, the computer program is making a simple two-way choice–the prepositional phrase is either modifying the verb (to see), or it’s modifying the noun (the man).  (The technical term for a two-way choice is a binary decision.)  Conceptually, it’s pretty straightforward.

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Cats keep showing up in these illustrations because the latest-and-greatest thing in machine learning is alleged to have solved all extant problems and made the rest of computer science irrelevant, but the major reported accomplishment so far has been classifying pictures as to whether or not they are pictures of cats.  The “It uses a few CPUSs!” part is a reference to the fact that in order to do this, it requires outlandish amounts of computing resources (a CPU is a “central processing unit”).  Picture source: https://doubleclix.wordpress.com/2013/06/01/deep-learning-next-frontier-01/

When you are trying to create a computer program to do something like this, you need to be able to understand how it goes wrong.  (Generally, seeing how something goes right isn’t that interesting, and not necessarily that useful, either.  It’s the fuck-ups that you need to understand.)  There are two concepts that are useful in thinking your way through this kind of thing, neither of which I’ve really understood–until now.

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Picture source: http://daco.io/insights/2016/about-deep-learning/

I recently spent a week in Constanta, Romania, teaching at–and attending–the EUROLAN summer school on biomedical natural language processing.  “Natural” language means human language, as opposed to computer languages.  Language processing is getting computer programs to do things with language.  Biomedical language is a somewhat broad term that includes the language that appears in health records, the language of scientific journal articles, and more distant things like social media posts about health.  My colleagues Pierre Zweigenbaum and Eric Gaussier taught a great course on machine learning, and one of the best things that I got out of it was these two concepts: bias and variance.  

Bias means how far, on average, you are from being correct.  If you think about shooting at a target, low bias means that on average, you’re not very far from the center.  Think about these two shooters.  Their patterns are quite different, but in one way, they’re the same: on average, they’re not very far from the center of the target.  How can that be the case for the guy on the right?

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Picture source: XX

Think about it this way: sometimes he’s a few inches off to the left of the center of the target, and sometimes he’s a few inches off to the right.  Those average out to being in the center.  Sometimes he’s a few inches above the target, and sometimes he’s a few inches below it: those average out to being in the center.  (This is how the Republicans can give exceptionally wealthy households a huge tax cut, and give middle-class households a tiny tax cut, and then claim that the average household gets a nice tax cut.  Cut one guy’s taxes by 1,000,000 dollars and nine guys’ taxes by zero (each), and the average guy gets a tax cut of 100,000 dollars.  One little problem: nobody’s “average.”)  So, he’s a shitty shooter, but on average, he looks good on paper.  These differences in where your shots land are are called variance.  Variance means how much your results differ from each other, on average.  The guy on the right is on average close to the target, but his high variance means that his “average” closeness to the target doesn’t tell you much about where any particular bullet will land.

Thinking about this from the perspective of the zombie apocalypse: variance means how much your results differ from each other, on average, right?  Low variance means that if you fire multiple times, on average there isn’t that much difference in where you hit.  High variance means that if you fire multiple times, there is, on average, a lot of difference between where you hit with those multiple shots.  The guy on the left below (scroll down a bit) has low bias and low variance–he tends to hit in roughly the same area of the target every time that he shoots (low variance), and that area is not very far from the center of the target (low bias).  The guy on the right has low bias, just like the guy on the left–on average, he’s not far off from the center of the target.  But, he has high variance–you never really know where that guy is going to hit.  Sometimes he gets lucky and hits right in the center, but equally often, he’s way the hell off–you just don’t know what to expect from that guy.

We’ve been talking about variance in the context of two shooters with low bias–two shooters who, on average, are not far off from the center of the target.  Let’s look at the situations of high and low variance in the context of high bias.  See the picture below: on average, both of these guys are relatively far from the center of the target, so we would describe them as having high bias.  But, their patterns are very different: the guy on the left tends to hit somewhere in a small area–he has low variance.  The guy on the right, on the other hand, tends to have quite a bit of variability between shots: he has high variance.  Neither of these guys is exactly “on target,” but there’s a big difference: if you can get the guy on the left to reduce his bias (i.e. get that small area of his close to the center of the target), you’ve got a guy who you would want to have in your post-zombie-apocalypse little band of survivors.  The guy on the right–well, he’s going to get eaten.

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High bias: both of the shooters tend to hit fairly far from the center of the target. The guy on the left has low variance, while the guy on the right has high variance.

A quick detour back to machine learning: suppose that you test your classifier (the computer program that’s making binary choices) with 100 test cases.  You do that ten times.  If it’s got an average accuracy of 90, and its accuracy is always in the range of 88 to 92, you’re going to be very happy–you’ve got low bias (on average, you’re pretty close to 100), and you’ve got low variance–you’re pretty sure what your output is going to be like if you do the test an 11th time.

Abstract things like machine learning are all very well and good for cocktail-party chat (well, if the cocktail party is the reception for the annual meeting of the Association for Computational Linguistics–otherwise, if you start talking about machine learning at a cocktail party, you should not be surprised if that pretty girl/handsome guy that you’re talking to suddenly discovers that they need to freshen their drink/go to the bathroom/leave with somebody other than you.  Learn some social skills, bordel de merde !)  So, let’s refocus this conversation on something that’s actually important: when the zombie apocalypse comes, who will you want to have in your little band of survivors?  And: why? “Who” is easy–you want Rick, Carol, Darryl.  (Some other folks, too, of course–but, these are the obvious choices.)  Why them, though?  Think back to those targets.

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Picture source: http://hubwav.com/moral-codes-walking-dead-characters-get-broken/

Low bias, low variance: this is the guy who is always going to hit that zombie right in the center of the forehead.  This is Rick Grimes.  Right in the center of the forehead: that’s low bias.  Always: that’s low variance.

Low bias, high variance: this is the guy who on average will not be far from the target, but any individual shot may hit quite far from the target.  This guy “looks good on paper” (explained in the English notes below) because the average of all shots is nicely on target, but in practice, he doesn’t do you much good.  This guy survives because of everyone else, but doesn’t necessarily contribute very much.  In machine learning research, this is the worst, as far as I’m concerned–people don’t usually report measures of dispersion (numbers that tell you how much their performance varies over the course of multiple attempts to do whatever they’re trying to do), so you can have a system that looks good because the average is on target, even though the actual attempts rarely are.   On The Walking Dead, this is Eugene–typically, he fucks up, but every once in a rare while, he does something brilliantly wonderful.

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High bias: both of the shooters tend to hit fairly far from the center of the target. Picture source: XX
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SPOILER AERT! The Walking Dead’s Carol. She starts out as a meek, mild, battered housewife who can barely summon up the courage to keep her daughter from being sexually abused. Later… Yes, she’s my favorite TWD character. Picture source: https://goo.gl/8D6323

High bias, low variance: this guy doesn’t do exactly what one might hope, but he’s reliable, consistent–although he might not do what you want him to do, you have a pretty good idea of what he’s going to do.  You can make plans that include this guy.  He’s fixable–since he’s already got low variance, if you can get him to shift the center of his pattern to the center of the target, he’s going to become a low bias, low variance guy–another Rick Grimes. This is Daryl, or maybe Carol.

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The Walking Dead’s Eugene. Picture source: https://goo.gl/CWsfqm

High bias, high variance: this guy is all over the place–except where you want him.  He could get lucky once in a while, but you have no fucking idea when that will happen, if ever.  This is the preacher.

Which Walking Dead character am I?  Test results show that I am, in fact, Maggie.  I can live with that.


Here are some exercises on applying the ideas of bias and variance to parts of your life that don’t have anything to do (as far as I know) with machine learning.  Scroll down past each question for its answer, and if you think that I got wrong, please straighten me out in the Comments section.  Or, just skip straight to the French and English notes at the end of the post–your zombie apocalypse, your choice.

  1. Your train is supposed to show up at 6 AM.  It is always exactly 30 minutes late.  If we assume that 30 minutes is a lot of time, then the bias is high/low.  Since the train is always late by the same amount of time, the variance is high/low.
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Cat pictures, cat pictures, cat pictures–do they talk of nothing but cat pictures?  ‘Fraid so… Picture source: https://www.slideshare.net/ITARENA/fishman-deep-learning
  1. The bias is high.  Bias is how far off you are, on average, from the target.  We decided that 30 minutes is a lot of time, so the train is always off by a lot, so the bias is high.  On the other hand, the variance is low.  Variance is how consistent the train is, and it is absolutely consistent, since it is always 30 minutes.  Thus: the variance is low.

Your train is supposed to show up at 6 AM.  It is always either exactly 30 minutes early, or 30 minutes late.  More specifically: half of the time it is 30 minutes early, and half of the time it is 30 minutes late.  Assume that 30 minutes is a lot of time: is the bias high or low?  Is the variance high or low?

deep-learning-on-hadoopspark-nextml-5-638
“DL” is “deep learning,” the most popular name for the latest-and-greatest approach to machine learning. I hear that it’s really good at recognizing pictures of cats.  Picture source: https://www.slideshare.net/agibsonccc/deep-learning-on-hadoopspark-galvanize

Since on average, the train is on time–being early half the time and late half the time averages out to always being on time–the bias is low. Zero, in fact.  This gives you some insight into why averages are not that useful if you’re trying to figure out whether or not something operates well. The give-away is the variance—even when something looks fine on average, high variance gives away how shitty it is.

Want to know which Walking Dead character you are?  You have two options:

  1. Take one of the many on-line quizzes available.
  2. Analyze yourself in terms of bias and variance.

English notes

low-rent: “having little prestige; inferior or shoddy” (Google) “low in character, cost, or prestige” (Merriam-Webster)

to look good on paper: “to seem fine in theory, but not perhaps in practice; to appear to be a good plan.” (McGraw-Hill Dictionary of American Idioms and Phrasal Verbs) Often followed by “but…”


French notes

From the French-language Wikipedia article on what’s called in English the bias-variance tradeoff:

En statistique et en apprentissage automatique, le dilemme (ou compromis) biais–variance est le problème de minimiser simultanément deux sources d’erreurs qui empêchent les algorithmes d’apprentissage supervisé de généraliser au-delà de leur échantillon d’apprentissage :

  • Le biais est l’erreur provenant d’hypothèses erronées dans l’algorithme d’apprentissage. Un biais élevé peut être lié à un algorithme qui manque de relations pertinentes entre les données en entrée et les sorties prévues (sous-apprentissage).
  • La variance est l’erreur due à la sensibilité aux petites fluctuations de l’échantillon d’apprentissage. Une variance élevée peut entraîner un surapprentissage, c’est-à-dire modéliser le bruit aléatoire des données d’apprentissage plutôt que les sorties prévues.

 

Fashion!

Why do computational linguistics?  Fashion! 

When I was in graduate school–in the US–I had a colleague whose child was allegedly growing up francophone.  I think the father was an American professor in the French department, or something.  We were all very impressed.

One semester we had a visiting academic from France in our lab.  He had super-hip glasses.  Over lunch one day, the kid asked him: “why do your glasses have such tiny lenses?  His response: c’est à la mode.  

The kid thought for a minute.  Then, another question: “why do you have ice cream on your glasses?”  I try not to be mean, but I thought to myself: this kid speaks French even worse than I do, and that’s an accomplishment…


Why do computational linguistics?  There are a lot of perfectly good reasons, but this guy has the best: Fashion!  Want to know more?  Check out the OpenMINTED project.  In the following material, “TDM” stands for text data mining.

In the French notes today (scroll down past the picture): my attempts to understand various words that could be used to translate the English word fashion.

 

TDM-story-sheet-Alan-Akbik-page-001
Source: openminted.eu

French notes

la mode : style, fashion, trend; the fashion industry itself.

la tendance : trend, fashion.

tendance (adj.) : trendy, fashionable, “in.”  Register: familier.

branché : trendy, cool, hip, “in.”  Register: familier.

le branché : cool person.

In trying to figure out the differences between la mode and la tendance via looking at examples on Linguee.fr, the trend (ha) seems to be that la tendance is not used to talk about things that are “in fashion” so much as tendencies/trends more generally.  The closest uses to “in fashion” are their adjectival examples:

Screen Shot 2018-05-10 at 20.33.39
Source: screen shot from Linguee.fr.

Compare some nominal (noun) examples–their translations are more about trends in general, versus trends in the sense of things being fashionable:

Screen Shot 2018-05-10 at 20.36.46
Source: screen shot from Linguee.fr.

Linguee.fr gives a number of examples of avoir tendance à, translated as “to tend to:”

Screen Shot 2018-05-10 at 20.38.36
From Linguee.fr.

For fashion in the sense of haute couture and the like (yes, that’s the English term, too), la mode seems to be more common:

Screen Shot 2018-05-10 at 20.50.19
From Linguee.fr.

Change the gender to masculine — le mode — and you have senses along the lines of “mode” in English:

Screen Shot 2018-05-10 at 20.52.34
From Linguee.fr.

…and some fixed expressions (all examples from Linguee.fr):

  • le mode d’emploi : operating instructions, instruction manual, user guide
    • J’ai lu le mode d’emploi avant d’utiliser l’appareil.  I read the instruction manual before using the device.
    • Le mode d’emploi est fourni en cinq langues.  The operating instructions are provided in five languages.
    • Avant de nous contacter, veuillez vous assurer d’avoir respecte le dosage des produits et suivile mode d’emploi.  Before contacting us, please, make sure that
      you take the right dosage of the products and follow the instructions for use.
    • le mode de vie : lifestyle
    • le mode aperçu : preview mode

It seems so simple that it makes one wonder: why was I ever confused about this?  As it happens, I have a pretty good memory for the contexts in which I run into words, so I can tell you that the source of my confusion is an advertising poster that I saw in the metro one day.  I interpreted it (possibly incorrectly) as meaning something like “so you think you know what’s cool?”, and my recollection is that it said something like tu penses que tu connais la tendance?  Maybe it’s just that the aforementioned kid (ledit marmot) spoke French better than I thought, and I speak French even worse than I thought…