Author: zipfslaw1

Every once in a while an innocuous technical term suddenly enters public discourse with a bizarrely negative connotation.  I first noticed the phenomenon some years ago, when I saw a Republican politician accusing Hillary Clinton of “parsing.”  From the disgust with which he said it, he clearly seemed to feel that parsing was morally equivalent to puppy-drowning.  It seemed quite odd to me, since I’d only ever heard the word “parse” used to refer to the computer analysis of sentence structures.  The most recent word to suddenly find itself stigmatized by Republicans (yes, it does somehow always seem to be Republican politicians who are involved in this particular kind of linguistic bullshittery) is “encryption.”  Apparently encryption is now right up there with dirty bombs in terms of things that terrorists are about to use to kill us all.  (“All” might be an exaggeration.  I find it interesting that the United States had 33,169 firearm deaths in 2013–roughly 11 times as many deaths as on 9/11–and yet, Republicans seem to think that it’s important that we make firearms as widely available as possible.  I guess they just don’t like people very much.)  As a moderately technical person, this strikes me as odd, since I’ve always thought of encryption as that nifty mathematical technique (I was about to say “algorithm,” but I think the Republicans are down on that one now, too) that keeps you from intercepting my text messages, me from reading your Ashley Madison profile, and so on.

In between the Republican outrage over parsing and the current panic over encryption, we had the sudden appearance in the public consciousness of data mining.  As far as I knew up to that point, data mining was a bunch of statistical techniques for finding relationships between things.  Suddenly it was showing up in scary news stories–Google the phrase “data mining is evil” (you have to put the quotes around it to search for the phrase, as opposed to the individual words) and you will get 1,400 hits as of the time of writing (May 2016).

Besides being bemused by this intrusion of American know-nothingness into public discourse, I have a personal stake in the issue, because people often refer to what I do for a living as text data mining.  This is a misnomer–by its nature, data mining is not something that you can do with texts.  Bear with me and I’ll explain why, and then we’ll look at some French vocabulary for talking about all of this.

Data mining is basically about databases.  In a database, the statistical techniques of data mining can help you do things like discover that Republicans with HBO subscriptions are more likely to consider voting for Romney in a primary than Republicans who don’t have HBO subscriptions.  (Real one, if I remember the facts correctly.)  You can do that because you have a table in the database that tells who’s a Republican, a table that tells who has HBO subscriptions, and a table that tells you which members of a random sample told the interviewer that they would/wouldn’t consider voting for Romney in a primary.  Data mining is the science/art of figuring out what things are related (HBO subscription/willingness to vote for Romney) and what things aren’t related (making one up here: having bought an Escalade and being willing/unwilling to vote for Romney in a primary)–this among probably thousands and thousands of variables.  Doing data mining research requires things like knowing particular kinds of math, understanding how to sample a population, getting computers to do complicated calculations in a way that is time-efficient—stuff like that.

With data mining, you have that database, and you know what everything is.  With “text mining,” or “text data mining,” as some people call it, you have texts, and you don’t know what anything is.  (By “you,” I mean a computer program.)  This is usually talked about as a difference between “structured” data (i.e., the database)–you know what everything “is”–what it “means”–in some sense, its semantics.  Whoops–that sentence got a little out of control.  “Unstructured” data: that’s typically how we would describe text.  With text, you know what nothing is–you don’t know what anything means–in a very literal sense, you don’t know its semantics.

“Text mining” could be thought of as turning unstructured data into structured data.  You’ve got a bunch of texts, and you want to use it to populate a database, perhaps.  Maybe you have 23 million journal articles in the National Library of Medicine, and you want to find every statement that those 23 million articles make about which genes are affected by which drugs.  Maybe you have a huge collection of French fairy tales, and you want (the computer) to find every time that a stepmother is mentioned and whether the portrayal of the stepmother is positive or negative.  You could think of both of those as turning unstructured data into structured data–you’re taking that unstructured data and using it to build a database about drugs and proteins, or a database about stepmothers.  You can see now why we tend to prefer the term “text mining” to “text data mining”–to the extent that “data mining” is about structured data, it doesn’t really make sense to talk about “data mining” with respect to language.  Where the data mining person basically just needs to know math, the text mining person needs to know something about how people write about whatever it is that you’re interested in.  I do a bit of text mining.  People will have really specific requests–tell me whether or not the genes from some experiment show up in the cancer literature, say; tell me if this is a suicide note or not; read this doctor’s note and tell me if this kid is a candidate for epilepsy surgery; stuff like that.  It’s not really linguistics, but it pays the bills, and it suits my need to do something that might actually make the world a better place.

A related field is natural language processing.  Natural language means human language, as opposed to computer languages.  Natural language processing is about building tools to handle specific linguistic tasks–parse a sentence, figure out parts of speech, stuff like that.  You might use a combination of different language processing programs to do a text mining task.  I find this more interesting, since the questions are less about some set of facts than they are about the language itself.  Where the data mining person needs to know math and the text mining person needs to know how people write about genes and drugs, or stepmothers, or whatever, the natural language processing person needs to know something about language itself–what kinds of structures sentences can have, how word frequencies are distributed, how to build linguistic resources for letting a computer process things that can’t be directly observed (e.g. semantics).  I do a lot of this kind of stuff.  Recently I’ve been working on coreference resolution–how to get a computer to recognize that Obama, President Obama, and Barak Obama are all referring to the same thing in the world, while Mrs. Obama and Michelle Obama are referring to something else in the world.  (Recognizing that those “things” in the world are people, as opposed to, say, locations, or the names of companies, is a whole different story.)

Yet another field is computational linguistics.  This is about using computational models to test theories about language.  This is my favorite, but it’s the hardest to pay the bills with.  I do some of this, too.  Nowadays a lot of my time goes into large-scale attempts to model the semantics of biomedical language.  I’m trying to investigate differences in the semantic primitives of biomedical language versus “general” English by building a large set of data-driven semantic representations of predicates found in journal articles; I’ll then compare that resource to a similar resource built for general English and look for things like whether or not the semantic primitives seem to come from the same set, whether or not given verbs have different representations in the two types of language, etc.  My hope is to get a sense of the range of types of semantic variability from this particular project.  You could imagine using computational linguistics work to build natural language processing tools, and then using those to carry out practical text mining tasks.  You could use the text “data” mining results to do actual data mining.

As you can tell from my examples, I’m very much in the world of biomedical language.  There’s also a lot that you can do in the humanities with this kind of stuff.  A hot topic in the future might be using mathematical representations of semantics to study things that are/are not thought of as binaries–gender, sexuality, race, political economy, whatever.  However, I would not claim to do ANY of that–I can just barely explain it.  For more on that kind of stuff, see this excellent post by Ben Schmidt.

In practice, even people in the field don’t always differentiate between these terms, or at least don’t draw sharp boundaries between them.  My business card says that I’m the director of a text mining group, but I identify most strongly as a computational linguist.  We figured that “text mining” makes more sense as a practical field of inquiry to have within a medical school (which is where I work), so that’s what we called the group when we formed it.  If you go to the annual conference of the Association for Computational Linguistics, you will see almost no computational linguistics, but rather a ton of natural language processing.  If you go to the annual Biomedical Natural Language Processing meeting, you’ll see a mix of text mining, natural language processing, and a bit of computational linguistics.  Sometimes the distinctions really matter, though.  This post started its life as a response to someone who asked me to be on a panel about data mining, to talk specifically about text data mining.  When I responded that I don’t do data mining, they asked what the difference is–this blog post started out as my response.

As far as I can tell, the relevant community in France doesn’t make these distinctions in any kind of rigid fashion, either, despite the much-vaunted French penchant for categorization (see Nadeau and Barlow’s excellent book for a discussion of where it comes from).  However, French does have technical vocabulary for all of these fields.  Here it is:

• fouiller: to excavate; to rummage through, to search (see also here)
• la fouille de données: data mining
• la fouille de texte(s): text mining
• le traitement automatique des langues naturelles: natural language processing
• la linguistique informatique: computational linguistics

The typical stereotype of Paris is as a beautiful, majestically historical city that just oozes romance, and indeed, Paris is all that.  But, visitors are often surprised to find that it is also a city with a sometimes astounding number of beggars on the street. The reasons behind this are many, and varied, and, I think, interesting.

In the pre-modern period, the vast majority of the French (like the vast majority of everyone else in the world) were farmers.  Most children didn’t live to adulthood, and you needed a lot of hands to work the farm, so people had big families.

In the 1500s, the French death rate took a relatively sudden drop.  People were still having those big families, so there were a relatively large number of people making it to adulthood.  The inheritance laws of the time included primogeniture, i.e. inheritance of everything by the oldest son, so lots of those people wouldn’t have a farm of their own to work.  Options were limited, and if they couldn’t find other employment, a lot of people hit the road.  (There’s an excellent description of the mechanics of this phenomenon in Robert Darnton’s The Great Cat Massacre and other episodes in French cultural history.)

If you hit the road in France, you’re eventually going to end up in Paris, if for no other reason than that it’s the hub of the road system (and today, the rail system).  If you can’t find other employment, your options come down to begging or stealing, and most people aren’t thieves.  So: begging.

Begging actually has a very long and somewhat respectable history in Europe.  As Robert Cole puts it: “In the middle ages, ‘Christian charity’ perceived the poor as God’s special children and therefore deserving of alms.”  Begging can be a profession, really.  (Old Eastern European Jewish joke: beggar hits a guy up for money.  Guy gives him some helpful hints on improving his approach.  Beggar responds: YOU’RE telling ME how to beg?  This would make total sense in a French context: a métier (profession) is a métier, whether you’re a doctor, an engineer, or an elevator operator.)

If you’re gonna be a beggar, though, it helps to have a schtick.  Physical lack of ability to work was a good one, and Parisian beggars were known for faking such a disability, leading to their squatting areas being known as Cours des miracles (“Courts of miracles”) for their recovery at the end of the working day.  (There was one just to the north of what is now the Place des Vosges, I believe.)  By the 1500s, begging wasn’t viewed quite as kindly.  Robert Cole again:

In sixteenth-century Paris the poor were viewed as merely layabouts who preferred to live off public welfare.  Meanwhile bad harvests, plagues, inflation and religious war increased their number dramatically.  Public begging was outlawed in 1536, and in 1551 laws were enacted which limited eligibility for public assistance and forbad women to have their children in tow when selling candles outside churches.  To do so, went the rationale, evoked sympathy from prospective customer, which proved that such women were really only begging.  A traveller’s history of Paris.

So: there have been a lot of beggars in Paris for centuries.  In 2007, the European Union was enlarged to include a couple countries with large Roma populations.  There have always been Roma in France, but now a lot more came (the Roma rights group FNASAT says 12,000 currently, and that’s after 10,000 being expelled in 2009 and another 8,000 in 2011; other estimates range from 20,000 to 400,000), and they are a prominent part of the Parisian begging ecosystem.  (There is, indeed, a Parisian begging ecosystem, and there are actually a number of distinct genres of begging in Paris–a subject in and of itself.)

To be clear: if you don’t give charity, your life is pointless.  Let me point out that this is a teaching of at least Christianity, Judaism, Islam, and Hinduism, and–for my fellow secularists in France–Rousseau, the revolutionary Constituent Assembly, National Convention, and Directory, and modern French philosophers from Sartre to Alain Finkielkraut.  (All of those links are to citations on the subject, not to their biographies.)  The Buddhist view of charity is especially appealing to me, as a (really bad) student of judo:

Buddhism views charity as an act to reduce personal greed which is an unwholesome mental state which hinders spiritual progress.  What Buddhists believe, Venerable K. Sri Dhammananda Maha Thera.

Judo’s view of the best human relationships is mutual welfare–we’re taught that human interactions should be mutually beneficial.  So, if it’s the case that charity benefits both the giver and the receiver, then it’s very judo.  Seriously, give charity–if for no other reason than that you’ll feel better about humanity if you take part in it being more humane.

• le mendiant: beggar.
• le gueux/la gueuse: beggar (literary).  A number of other, more pejorative meanings–highwayman for men, whore for women, etc.  Probably obsolete, but keep it mind for when you read Tartuffe.
• le clochard: beggar; also bum.  (Slang.)
  
• le/la clodo: beggar; also homeless person, tramp, hobo.

Some additions from native speaker Phildange:

• le vagabond: wandering beggar, hobo.
• le chemineau: same as above.
• faire la manche: to beg.

I was in the Navy with an Armenian woman.  (No, you don’t have to be a citizen to serve in the American military, and that’s probably true in most countries.  In France, you can get citizenship by serving in the military–you are français par le sang versé, “French by spilt blood.”  This isn’t the case in the United States–you can apply for citizenship as a member of our military, but there actually isn’t any guarantee that you’ll get it.) We’ll call her Nairi (not her real name).  Like many members of the Armenian diaspora, Nairi was massively multilingual–she spoke Armenian, Arabic, and Spanish natively, and French and English as very strong second languages.  (I once saw her mother test her to make sure that she wasn’t forgetting any of them.)  One day Nairi came back from leave (what we call vacation in the military) with a seven-language dictionary.  I admired it, and she insisted that I take it.  I refused, she insisted, I refused, she insisted, I refused, she insisted, and finally, I took it.  What I didn’t realize was that in Armenian culture, if someone admires something of yours, you must insist that they take it.  Armenians know that they most certainly should not take it–I didn’t.  Now I do.  Stupid me–every time I see that dictionary on my bookshelf, I feel like a total jerk.

In a recent post, we talked about monolingual dictionaries–that is, dictionaries that list words in some language and give definitions of them in that same language.  Today, let’s talk about bilingual dictionaries–that is, words that list words in some language and give corresponding words in another language.  Of course, anything that we might say about bilingual dictionaries applies equally to dictionaries with even more languages, like the one that I stupidly took from poor Nairi.

I carefully said “corresponding” words just above–I carefully didn’t say “equivalent” or “the same” words.  This is because it’s often the case that there isn’t a single translation from one word in one language to one word in another language.  Even when there is one, it doesn’t necessarily “mean” the same thing, in some sense of the word “meaning.”  To give you an example from my college French 101 textbook: a fenêtre in French is a window in English–fine so far.  But, say window in English, and the referent is most likely a casement window, specifically–one that slides up and down.  Say fenêtre in French, and the reference is most likely a window that opens in the middle–horizontally.  (We would call this a French window in English.  See this post for a list of things that we call French something-or-other in English that aren’t called anything of the sort in French.)  And, as I said, there often isn’t just one.  A language that I worked on in grad school has the word invert.  But: invert what?  If you’re inverting a hollow object, that’s one verb–if you’re inverting a solid object, it’s another verb.  French has maybe two words for snow–la neige, and la poudreuse (powder snow).  Depending on how you count, English has 13 or 55 or 120 (scroll down past the Inuit words) or 182 words for snow.  So: not a 1-to-1 correspondence.

Having at least mentioned some of the theoretical issues, let’s look at the practical points of buying and using a bilingual dictionary.  In these days of Amazon, you can use reader reviews in a way that we never could before–it’s really a nice advantage over the old pre-Internet days.  However, there are also some specific things to look for.

• Example sentences: you want a dictionary with example sentences, at least in the language that’s foreign to you.
• Verb + preposition combinations: a good dictionary should tell you which prepositions, if any, go with which verbs.  You need to know, for instance, that in English you shoot at something, you lean toward (have a preference for) something, and you stop doing something, with no preposition.  Likewise, in French you need to know that you tirer sur or tirer contre (shoot “on” or shoot “against”) something, you pencher pour (lean “for”) something, and you arrêter de (stop “from”) doing something.
• If you are working with language(s) that have gender, you want the gender to show up both in the Language1 -> Language2 section and in the Language2 -> Language1 section.  If you look up kitchen towel and find that the translation to French is torchon, you don’t want to then have to go to the French -> English section to see whether it’s le torchon (it is) or la torchon (it isn’t).
• This might seem obvious, but make sure that the pronunciation is given for the words in any language whose pronunciation isn’t obvious from the spelling–and, yes, that includes both English and French.
• This takes a while, but: when you find the word that you’re looking for in the other language, you might want to look it up in the other direction.  For example: suppose that you look up the English word towel in a crappy bilingual English/French dictionary.  In a crappy dictionary, you might find the following: serviette, torchon.  Both of those can, indeed, be used to translate towel from English to French–but, they’re not equivalent.  Serviette is for a bath or beach towel, while torchon is for a kitchen towel.  You want a dictionary that will distinguish between the various possible translations.  It’s often useful to look the French words up in turn (or the English words, if you’re going from French to English).  If you do that, you’ll find that a serviette can be a towel, but also a napkin, or a briefcase.  A torchon, you’ll find, can also be a messy document, or a rag.  It’s good to be on top of this kind of thing when you’re trying to choose between supposed synonyms.
• Labelling of registers, or levels of appropriateness: you most definitely want a dictionary that includes slang, obscenities, informal words, etc., or you’re not going to get very far in real life.  However, you also want a dictionary that labels words that are non-standard–offensive words, etc.  This kind of thing can be really, really hard to catch when you’re learning a language from movies, your neighbors, etc.

The always-awesome Lawless French web site has a good page on the subject of how to use a bilingual dictionary, and it has much better examples than I do.  You can find it here.

So, what are some good bilingual English/French dictionaries?  Here are some options.

• The best thing out there these days is almost certainly WordReference.com.  It has lots of language pairs, example sentences, colloquial expressions, pronunciations, male and female forms of adjectives, plurals, a verb conjugator, and a reverse look-up feature that does exactly what I suggest you do in the last bulletted item above.  The auto-c0mplete feature in the search box saves me enormous amounts of time (and guessing about spellings).  There’s an excellent WordReference iPhone app.  Be aware, though, that the iPhone app will not generally let you look up obscenities–you have to go to the web site for that.
• For the Kindle or for the Kindle app on your phone, the Collins English-French and French-English dictionaries are quite good.  They’re quite highly rated on Amazon.com.  I have the Collins dictionaries on my phone, and use them whenever I don’t have Internet access and therefore can’t get to WordReference.com.  The Collins dictionaries also have an advantage over WordReference: they don’t give as many super-subtle translations.  The only bad thing about WordReference is that it can sometimes give an overwhelming number of other-language translations.  That’s great when you want it, but when you don’t, you might prefer the Collins dictionary.  As it happens, there is a Collins dictionary tab on the WordReference site, and it’s easy to click on that.
• Linguee.fr is fantastic for seeing things in context.  You will generally get lots of example sentences.  There’s an iPhone app for that, too.
• Reverso.net is another good one for seeing things in context.  It sometimes has better coverage of colloquial, slang, and obscene language than Linguee does.  Again, there’s an iPhone app.

I found Nairi on Facebook recently.  I sent her a friend request–no response.  Is it because she doesn’t remember who the hell I am?  Is it because she hates me for taking her dictionary?  I have no idea.  Nairi, if you’re reading this: I’m sorry!