Imagine you have a sequence of snapshots from a day in Justin Bieber’s life, and you want to label each image with the activity it represents (eating, sleeping, driving, etc.). How can you do this?
One way is to ignore the sequential nature of the snapshots, and build a per-image classifier. For example, given a month’s worth of labeled snapshots, you might learn that dark images taken at 6am tend to be about sleeping, images with lots of bright colors tend to be about dancing, images of cars are about driving, and so on.
By ignoring this sequential aspect, however, you lose a lot of information. For example, what happens if you see a close-up picture of a mouth – is it about singing or eating? If you know that the previous image is a picture of Justin Bieber eating or cooking, then it’s more likely this picture is about eating; if, however, the previous image contains Justin Bieber singing or dancing, then this one probably shows him singing as well.
Thus, to increase the accuracy of our labeler, we should incorporate the labels of nearby photos, and this is precisely what a conditional random field does.
Let’s go into some more detail, using the more common example of part-of-speech tagging.
In POS tagging, the goal is to label a sentence (a sequence of words or tokens) with tags like ADJECTIVE, NOUN, PREPOSITION, VERB, ADVERB, ARTICLE.
For example, given the sentence “Bob drank coffee at Starbucks”, the labeling might be “Bob (NOUN) drank (VERB) coffee (NOUN) at (PREPOSITION) Starbucks (NOUN)”.
So let’s build a conditional random field to label sentences with their parts of speech. Just like any classifier, we’ll first need to decide on a set of feature functions fi.
In a CRF, each feature function is a function that takes in as input:
and outputs a real-valued number (though the numbers are often just either 0 or 1).
(Note: by restricting our features to depend on only the current and previous labels, rather than arbitrary labels throughout the sentence, I’m actually building the special case of a linear-chain CRF. For simplicity, I’m going to ignore general CRFs in this post.)
For example, one possible feature function could measure how much we suspect that the current word should be labeled as an adjective given that the previous word is “very”.
Next, assign each feature function fj a weight λj (I’ll talk below about how to learn these weights from the data). Given a sentence s, we can now score a labeling l of s by adding up the weighted features over all words in the sentence:
(The first sum runs over each feature function j, and the inner sum runs over each position i of the sentence.)
Finally, we can transform these scores into probabilities p(l|s) between 0 and 1 by exponentiating and normalizing:
So what do these feature functions look like? Examples of POS tagging features could include:
f1(s,i,li,li−1)=1 if li= ADVERB and the ith word ends in “-ly”; 0 otherwise. ** If the weight λ1 associated with this feature is large and positive, then this feature is essentially saying that we prefer labelings where words ending in -ly get labeled as ADVERB.
f2(s,i,li,li−1)=1 if i=1, li= VERB, and the sentence ends in a question mark; 0 otherwise. ** Again, if the weight λ2 associated with this feature is large and positive, then labelings that assign VERB to the first word in a question (e.g., “Is this a sentence beginning with a verb?”) are preferred.
f3(s,i,li,li−1)=1 if li−1= ADJECTIVE and li= NOUN; 0 otherwise. ** Again, a positive weight for this feature means that adjectives tend to be followed by nouns.
f4(s,i,li,li−1)=1 if li−1= PREPOSITION and li= PREPOSITION. ** A negative weight λ4 for this function would mean that prepositions don’t tend to follow prepositions, so we should avoid labelings where this happens.
And that’s it! To sum up: to build a conditional random field, you just define a bunch of feature functions (which can depend on the entire sentence, a current position, and nearby labels), assign them weights, and add them all together, transforming at the end to a probability if necessary.
Now let’s step back and compare CRFs to some other common machine learning techniques.
The form of the CRF probabilities p(l|s)=exp[∑mj=1∑ni=1fj(s,i,li,li−1)]∑l′exp[∑mj=1∑ni=1fj(s,i,l′i,l′i−1)] might look familiar.
That’s because CRFs are indeed basically the sequential version of logistic regression: whereas logistic regression is a log-linear model for classification, CRFs are a log-linear model for sequential labels.
Recall that Hidden Markov Models are another model for part-of-speech tagging (and sequential labeling in general). Whereas CRFs throw any bunch of functions together to get a label score, HMMs take a generative approach to labeling, defining
So how do HMMs compare to CRFs? CRFs are more powerful – they can model everything HMMs can and more. One way of seeing this is as follows.
Note that the log of the HMM probability is logp(l,s)=logp(l0)+∑ilogp(li|li−1)+∑ilogp(wi|li). This has exactly the log-linear form of a CRF if we consider these log-probabilities to be the weights associated to binary transition and emission indicator features.
That is, we can build a CRF equivalent to any HMM by…
Thus, the score p(l|s) computed by a CRF using these feature functions is precisely proportional to the score computed by the associated HMM, and so every HMM is equivalent to some CRF.
However, CRFs can model a much richer set of label distributions as well, for two main reasons:
Let’s go back to the question of how to learn the feature weights in a CRF. One way, unsurprisingly, is to use gradient descent.
Assume we have a bunch of training examples (sentences and associated part-of-speech labels). Randomly initialize the weights of our CRF model. To shift these randomly initialized weights to the correct ones, for each training example…
In other words, every step takes the difference between what we want the model to learn and the model’s current state, and moves λi in the direction of this difference.
Suppose we’ve trained our CRF model, and now a new sentence comes in. How do we do label it?
The naive way is to calculate p(l|s) for every possible labeling l, and then choose the label that maximizes this probability. However, since there are km possible labels for a tag set of size k and a sentence of length m, this approach would have to check an exponential number of labels.
A better way is to realize that (linear-chain) CRFs satisfy an optimal substructure property that allows us to use a (polynomial-time) dynamic programming algorithm to find the optimal label, similar to the Viterbi algorithm for HMMs.
Okay, so part-of-speech tagging is kind of boring, and there are plenty of existing POS taggers out there. When might you use a CRF in real life?
Suppose you want to mine Twitter for the types of presents people received for Christmas:
What people on Twitter wanted for Christmas, and what they got: twitter.com/edchedch/statu…— Edwin Chen (@echen) January 2, 2012
How can you figure out which words refer to gifts?
To gather data for the graphs above, I simply looked for phrases of the form “I want XXX for Christmas” and “I got XXX for Christmas”. However, a more sophisticated CRF variant could use a GIFT part-of-speech-like tag (even adding other tags like GIFT-GIVER and GIFT-RECEIVER, to get even more information on who got what from whom) and treat this like a POS tagging problem. Features could be based around things like “this word is a GIFT if the previous word was a GIFT-RECEIVER and the word before that was ‘gave’” or “this word is a GIFT if the next two words are ‘for Christmas’”.