Linguistics 581

Smoothing language models


Note that most of the cells in our original count tables will be zero.

  1. We don't see many of the words in English.
  2. We don't see the huge majority of the bigrams of English.
  3. We see only a tiny sliver of all the possible trigramns

Most of the time our bigram model assigns probability zero to a potential following word:

Probability zero means it can't happen. But we aren't entitled to reach that conclusion.
basic idea

We add one to every cell of this table

We get this table

We recompute our our total occurrences:

  • I 3437 +1616 =5053
  • want 1215 + 1616 = 2931
  • to 3256 + 1616 = 4872
  • eat 938 + 1616 = 2554
  • Chinese 213 + 1616 = 1829
  • food 1506 + 1616 = 3122
  • lunch 459 + 1616 = 2075

Now we recompute the probabilities:

  • P(wn | wn-1) = |wn-1wn| ÷ |wn |
  • P(food | want) = |want to| ÷ |want| = 1 ÷ 2931 = .0003
  • P(to | want) = |want to| ÷ |want| = 787 ÷ 2931 = .27

This gives us this bigram probability table.

Compare this one.

Some things to notice:

  1. The events that used to be zeroes don't all have the same probability.
  2. All the events in the same row that were zeros in the old model get the same probability in the new model.
  3. ALL the non-zero probabilities went down.
  4. Sometimes the change doesn't look very large
    1. P(eat | I)[.0038 -> .0028]
    2. P(I | to)[.00092 -> .00082]
  5. Some very predictable events became less predictable:
    1. P(to|want)[.65-> .22]
    2. P(food|Chinese) [.56 -> .066]
  6. Other probabilities changed by large factors.
    1. P(lunch|Chinese) [.0047 -> .0011]
    2. P(food|want) [.0066 -> .0032]
  7. Likelihood ratios changed
    1. old model: P(I|lunch) = 4 * P(food|lunch)
    2. new model: P(I|lunch) = 2.5 * P(food|lunch)

Conclusion: Increasing the zero probabilities from zero to a small number was good, but the effect on the non-zero probabilities was not always good. We're blurring our original model.

  1. We've assigned too much probability to the zeros, with the result that sharply predictable events [P(to|want)] became much less so, and some moderately rare events became very rare.
  2. We want a model that changes the existing model less, but still steals away some probability to assign to the zero events.
What went

If we're going to assign the probability to zero-events, the probabilities of others has to go down.

Why? Because the probability of all the possible events we're looking at must add up to 1.

Take the case of want:

  • Count before smoothing: 1215
  • Count after smoothing: 1215 + 1616 = 2931
  • Number of word types not seen to follow want (estimating):
      Top 4 words (to, a, some, Thai) = .75 of the probability mass
      tokens not in top 4 = 304
            (.25 * 1215)
      a minimum of 1308 (1612 - 304) words never-before seen to follow want
  • This means that, in the model, following want, almost half of the probability mass is reserved for unseen events, 1308 events each of which has the probability 1/1308.
    • 1308 ÷ 2931 = .45
    • Which means the probability of all the previously seen words has to go down precipitously (1.0 -> .55)

    It's easy to see what the extreme case would be. Suppose the word to always followed the word want in our corpus but that want was a much rarer word, say, with count 100.

    Even in that case, we'd still have pretty good evidence that to was extremely likely after want. Our initial model would assign probability 1.

    What would happen with add-one smoothing?

    • Count before smoothing: 100
    • Count after smoothing: 100 + 1616 = 1716
    • Number of word types not seen to follow want: 1615.
    • probability for unseen events: 1615 / 1716 = .94
    • p(to|want) after smoothing = .06
Witten Bell
The Idea

Key idea; Some words are promiscuous (they occur with a wide variety of words relative to their frequency).

Some are faithful: They occur with a very small number of words given their frequency.

Our fictional example of want was a maximally faithful word. 100 occurrences all followed by the the same word to.

Key Idea: Find a way of measuring word promiscuity. Relativize the amount of prpobability mass a worfd receives for zero's to how promiscuopus it is.

    The more promiscuous a word is, the word probability mass it receives for following zeroes (the more likley it is that we havent seen all the words that can follow it in any given corpus).
Probability of a new event  

Probability oif seeing a new type:

    T ÷ (N + T)
T is the number of observed types. N is the number of words in corpus:
    N + T = the number of words plus the number of types
Corpus viewed as a set of N + T events.

We will use

    T ÷ (N + T)
as our estimate of how much probability mass to reserve for zeros.

if we divided this equally, anmd there are Z zero ngrams: each 0 ngram would get this much

    T ÷ (Z*(N + T))

We relativize the probability of seeing a new type to each wor w.

This becomes our promiscuity measure.

    T(w) ÷ (N(w) + T(w))
The number of word types following w (T(w)) divided by the sum of the number of word tokens following w (= c(w), the count of w) and types following w (T(w). For an absolutely faithful word like out fictional want, what is this?
    Total prob mass reserved for want = 1 ÷ (100 + 1)
How about a maxially promiscuous word with the same frequency:
    Total prob mass reserved for want = 100 ÷ (100 + 100)

Lets use Z(w) for the count of the words NOT seen to follow w. Then our new conditional probability for an unseen word has to be divided among those Z words:

    prob(w'|w) = T(w) ÷ (Z(w)(N(w) + T(w)))
where w' has never been seen to follow w (c(ww')=0).

The tricky thing thing that each probability for a seen bigram has to get reduced by the right amount to make everything add up to 1:

    p(w'|w)= c(ww') ÷ (c(w) + T(w))
where w' has never been seen to follow w (c(ww') is bigger than 0).

The bigram counts will add up to c(w) (=N(w)). So the total probability mass for SEEN bigrams following w will be:

    N(w) ÷ (N(w) + T(w))
leaving us:
    T(w) ÷ (N(w) + T(w))
for the unseen bigrams. And this agrees with the amount we decided to reserve for them!

Berkeley Restaurant example revisited:

  1. Witten-Bell smoothed bigram counts.
  2. Unsmoothed bigram counts.
  3. Add-One smoothed bigram counts.
Smoothed counts: Multiply smoothed probabilities by corpus size N.