On March 20, 2013

This article is part of the Machine Learning in Javascript series which teaches the essential machine learning algorithms using Javascript for examples. I use Javascript because it’s well-known and universally supported, making it an excellent language to use for teaching. There’s a mailing list at the bottom of the page if you want to know about new articles; you can also follow me on twitter: @bkanber.

Are you just looking for the code example? Scroll down!

Document classification is one of my favorite tasks. “Document classification” is exactly what you think it is: given a document and a set of labels, apply the most appropriate label to that document. Labels (or “classes” or “categories”) can be things like:

- “spam” or “not spam” (most mail clients use some form of Bayesian spam detection)
- “written by a male” or “written by a female” (yes, there are clues that can hint to the gender of the author)
- “technology”, “politics”, “finance”, “sports” (if you need to automatically categorize old articles from a newspaper)

Today we’re going to solve a simple problem: **language detection.** Put another way: “given a piece of text, determine if it’s in Spanish, English, or French”.

First, we’ll have to change the way we think about “documents”. At their most basic level, documents are just collections of arranged characters. Some characters may be letters, others are not. Letters make words, and words have meaning. Sometimes punctuation alters the meaning of words, other times it doesn’t matter too much! (See what I did there?) We typically study documents by studying the individual words in them. Sometimes, if the situation calls for it, we’ll look at more than one word at a time (word pairs are called “bigrams”), but since this is only “part 1″ of this Bayes series we’ll only consider unigrams for now.

Because natural language itself is so quirky and complicated, we generally try to simplify things; we try to reduce the *entropy* of the system. If you consider both uppercase and lowercase letters, you can create 7.3 million different four-letter words. If you limit yourself to only lowercase letters, that figure drops down to only 450,000. Why is this important? You want your system to use as much data as it can, and treating “YOU’RE” and “you’re” and “You’re” separately only serves to keep what you learn about each in separate buckets. Converting all of those to a simple “you’re” is best, because it’ll allow you to study the *concept* of the word “you’re” rather than the various syntaxes of the word.

Of course, more advanced data scientists will recognize that sometimes you *do *want these quirks in your system. When detecting spam emails, for instance, it turns out that “offer” and “OFFER” are two very different things. Often, spam detection algorithms will *not *normalize based on case because of this. Similarly, “money back” and “money back!!!!” have two very different meanings, so spam detection algorithms will generally also leave the punctuation intact.

You may also decide that you also want the meanings of the words “imagine”, “imagination”, and “imaginary” to be lumped together. To do so, you might chop off the ending of each, converting them to “imagin” — it’s neither verb nor noun, neither singular nor plural, it’s simply a concept. This is called “stemming”, and it serves to treat different words with the same meaning as the same entity. These entropy-reduction techniques are very important in machine learning as a whole, since training sets for learning algorithms are usually limited.

The process of splitting a document up into discrete chunks that you can study is called “tokenization”. For now, we’ll keep our tokenization simple: we’ll remove any punctuation, make everything lowercase, and split the document up by spaces to get our tokens.

I’m not going to talk about the math around Bayes’ theorem, because interested readers can easily learn the basics of probability and Bayes’ theorem on their own. Instead, we’ll aim to develop an intuition about this important tool in probability.

We’re trying to figure out which language a previously unseen document is in. We have a stack of pre-labeled documents in English, French, and Spanish. Since we decided above that we can learn about a document by inspecting the individual words in that document, let’s start there.

If you look at a single word in a document, you can easily figure out how many times it appeared in your training data. Using that information, you can determine the probability that a certain language will use a given word. For example: “vous” is the first word in your document of unknown origin. “Vous” may show up in all of your French documents (100%), no Spanish documents, and a small number of English documents (5%). That’s a great hint! The document is French!

But no, we can’t stop there. Just because “vous” is clearly a French word doesn’t mean the document itself is French. It may be an English novel quoting a French character. So we can’t just simply look at the “probability that ‘vous’ is French”, which is what we just tried. Instead, we need to determine the “probability that this document is French given that the word ‘vous’ is in it”. Fortunately, Bayes’ theorem does exactly that. If we apply Bayes’ theorem to the fake numbers I gave above, we find that there’s a 98% chance that a document is French if “vous” appears in it. The formula to calculate that is quite simple; see Bayes’ theorem.

Of course, if the next phrase in the document is “said Jean Pierre, the French museum curator”, we know there’s a much smaller chance of the document being French. So we look at each word in turn and calculate the “probability that the document is (French|English|Spanish) given that this word is in it”. We combine those individual probabilities and end up with an overall “probability that this document is French [given that all these words are in it]“. If that probability is high enough, you can act upon it.

The Naive Bayes Classifier is so named because it assumes that each word in the document has nothing to do with the next word. That’s a naive assumption. But it turns out that, while naive, it’s actually a great simplifying assumption; studying words separately like this actually yields very good results. You could also study bigrams or trigrams (sets of two or three words at a time), at which point the classifier is no longer “naive” but it’ll require a much larger amount of training data and storage space.

The reason the NB classifier works well for document classification is that it de-correlates the number of times a word is seen in a given language from its statistical importance. The word “a” is found in many languages. Perhaps it even appears in 100% of your English training set. But that doesn’t mean that documents that have it are English. We use Bayes to convert the “probability that ‘a’ appears in an English document” (which is 100%) to the “probability that this document is English because it has ‘a’ in it” (maybe 50%).

Therefore, the common stuff that’s found everywhere is given a very weak significance and the stuff that’s found more uniquely across a category is given a much stronger weight. The end result is a very smart, simple algorithm that has low error rates (“low” being a relative term). It’s not magic, there’s no neural network, there’s no “intelligence”, it’s just math and probability.

As usual, let’s just dive in. The first thing our document classifier needs to be able to do is train itself given a piece of text and a label for that text. Since I decided to build this classifier as generalized as possible, we won’t hard code the labels or even put a cap on the number of labels allowed. (Though naive Bayes classifiers with only two possible labels, like spam/ham, *are* a little bit easier to build.)

Bayes.train = function (text, label) { registerLabel(label); var words = tokenize(text); var length = words.length; for (var i = 0; i < length; i++) incrementStem(words[i], label); incrementDocCount(label); };

The `registerLabel`

function simply adds the label to the "database" (in this case, localStorage) so that we can retrieve a list of labels later.

The `tokenize`

function in this case is very simple. We'll look at more interesting tokenization techniques in another article (tokenization can be an important part of these classifiers), but this one is straight-forward:

var tokenize = function (text) { text = text.toLowerCase().replace(/\W/g, ' ').replace(/\s+/g, ' ').trim().split(' ').unique(); return text; };

In this case, we use `unique()`

because we're only interested in *whether* a word shows up in a document, and not the number of times it shows up. In certain situations, you may get better results by considering the number of times a word appears in a document.

We then loop through each word (or "token"), and call `incrementStem`

on it. This function is also very simple: it just records the number of times a word was seen for a given label.

Finally, we call `incrementDocCount`

, which records how many documents we've seen for a given label.

The end result of training is that we have a database that stores each label we've ever seen (in our example, it'll hold "english", "spanish", and "french"), stores the number of times a word has been seen for a label (eg, "le" was seen in French documents 30 times), and stores the total number of documents for each label (eg, we saw 40 French documents).

Training a naive Bayes classifier is dead simple and really fast, as demonstrated above. Guessing a label given a document is a little tougher, but writing the algorithm is easy to those who understand probability. If you don't understand probability, that's ok; you can spend some time reading up on naive Bayes classifiers and you'll always have this example to come back to and study.

The first thing we'll do in our guessing function (other than initializing variables; see the JSFiddle for the minutiae) is a little bit of bookkeeping:

for (var j = 0; j < labels.length; j++) { var label = labels[j]; docCounts[label] = docCount(label); docInverseCounts[label] = docInverseCount(label); totalDocCount += parseInt(docCounts[label]); }

Our goal here is to set ourselves up for calculating certain probabilities later. To do this, we need to know the number of documents we've seen for a given label (docCounts), but we also need to know the number of documents *not* in that label (docInverseCounts). Finally, we need to know the total number of documents we've ever seen.

You could flip this function upside down and get docInverseCount simply by subtracting a label's docCount from the totalDocCount -- in fact, that approach is better and faster, but I did it with an explicit docInverseCount function because it reads a little easier.

Given the above information we can determine, for example, the probability that any arbitrary document would be French. We also know the probability that any document is NOT French.

for (var j = 0; j < labels.length; j++) { var label = labels[j]; var logSum = 0; ...

Next, we look at each label. We set up a `logSum`

variable, which will store the probability that the document is in this label's category.

for (var i = 0; i < length; i++) { var word = words[i]; var _stemTotalCount = stemTotalCount(word); if (_stemTotalCount === 0) { continue; } else { var wordProbability = stemLabelCount(word, label) / docCounts[label]; var wordInverseProbability = stemInverseLabelCount(word, label) / docInverseCounts[label]; var wordicity = wordProbability / (wordProbability + wordInverseProbability); wordicity = ( (1 * 0.5) + (_stemTotalCount * wordicity) ) / ( 1 + _stemTotalCount ); if (wordicity === 0) wordicity = 0.01; else if (wordicity === 1) wordicity = 0.99; } logSum += (Math.log(1 - wordicity) - Math.log(wordicity)); } scores[label] = 1 / ( 1 + Math.exp(logSum) );

The above is the meat of the algorithm. For each label we're considering, we look at each word in the document. The _stemTotalCount variable holds the number of times we've seen that word in *any* document during training. If we've never seen this word before, just skip it! We don't have any information on it, so why use it?

`wordProbability`

represents the "probability that this word shows up in a [French|English|Spanish] document". If you've seen 40 French documents, and 30 of them have the word "le" in them, this value is 0.75. `wordInverseProbability`

is the probability that the word shows up in any other category than the one we're considering.

The funny `wordicity`

variable is what happens when you apply Bayes' theorem to the two probabilities above. While the wordProbability variable represents "the probability that [le] shows up in a [French] document", the wordicity variable represents "the probability that this document is [French] given that [le] is in it". The distinction is subtle but very important. If you're having trouble understanding the distinction at this point, I strongly recommend saying those two phrases out loud and making sure you understand the difference before moving on.

The wordicity line above also makes the assumption that English, French, and Spanish documents are all equally common and starting off on the same footing. This assumption makes the calculation a little simpler, but you can consider the *a priori* probabilities of each language if you'd like. A note on that later.

The line below the wordicity definition is an optional *adjustment* for words that we've only seen in training a few times. If you've only seen a word once, for instance, you don't really have enough information about that word to determine if it's really French or Spanish. So we make a weighted adjustment: we bring the wordicity closer to 50% if we haven't seen it too many times. The "0.5" in that equation is the value we should try to adjust towards, and the "1"s in the equation are the weight -- if you increase this value, the wordicity will remain close to 0.5 longer. If you have a large training set, you can make the weight 5 or 10 or 20 or 50 (depending on how big your training set is). Since we have a very small training set, I made this value 1 but realistically I should have just omitted the line completely (my training set is only 15 paragraphs). I just wanted to show you that adjusting for rare words is something that you can do.

Below that, we avoid letting wordicity be either 0 or 1 since we're about to use a log function on our data, and either of those values would kind of mess up the results.

The logSum line isn't really a part of the mathematical equations, but is rather a practical consideration. After calculating the wordicity for each word, we need to combine those probabilities somehow. The normal mathematical way to do that would be to multiply each probability together and divide by the multiplication of all the inverses. Unfortunately, floating point math isn't perfect and you can run into "floating point underflow", where the number gets too small for floating point math to deal with. So instead, we take a log of the numerator and denominator (combined probabilities and their inverses), and add up the logs.

Finally, after we've combined all the individual word probabilities with the logSum line, we undo the log function we just used to get the probability back in the 0 to 1 range. Note that this happens outside of the "look at each word" loop but still inside the "look at each label" loop.

One thing I'd like to point out is that the above makes a big simplifying assumption: we've assumed that English, French, and Spanish documents are all *equally likely* to appear. In our example, this is a good assumption since you guys are probably going to test one of each later, but in the real world this isn't necessarily true.

The Bayes classification algorithm does actually let you consider the *a priori* probability of a document's language (meaning, the probability that a document is English just based on the number of English documents out there, before considering the actual contents of the document), but I've simply left this out. It's not too hard to put in; adding just a few more terms to the wordicity calculation can do this. The next Bayes article I write will use the full form of Bayes theorem.

Finally, please note that I was *really lazy* while training this algorithm. You can see from the JSFiddle that I've only used 5 paragraphs from each language to train the thing on. That's not nearly enough to go by, as the words seen during training are the only words it knows. I've found that this example *does* work well if you type in sentences or paragraphs (try just copy/pasting stuff from news sites), but simple nouns and phrases probably won't work. For example, you'll get the wrong result for "la tortuga" ("the turtle" in Spanish) simply because we never showed it the word "tortuga" before. The algorithm will guess French in this case because it's seen slightly more "la"s in French than it's seen in Spanish. A larger training set would fix this issue.

We're basically done. All we have to do now is either report all the labels' probabilities or just pluck out the highest one. Try pasting some English, French, or Spanish news text in the JSFiddle below -- you should see the guessed language and the probability that led us to guess that language. This example works better with sentences or paragraphs; the more words you give it to guess by, the better the chance that it has seen one of those words in its limited training set.

So far, I've had 100% accuracy when copying and pasting sentences from news sites. Try it below and see for yourself!

If you like this article, or thought it was helpful, please consider:

- Sharing it with friends on Facebook and Twitter
- Discussing it on Hacker News and Reddit
- Discussing it in the comments section below
- Following me on Twitter @bkanber
- Reading my other ML in JS articles
- Signing up for the ML in JS mailing list below — I only send emails about new articles, never spam, won't ever sell your info