Presentation is loading. Please wait.

Presentation is loading. Please wait.

N-gram model limitations Important question was asked in class: what do we do about N-grams which were not in our training corpus? Answer given: we distribute.

Similar presentations


Presentation on theme: "N-gram model limitations Important question was asked in class: what do we do about N-grams which were not in our training corpus? Answer given: we distribute."— Presentation transcript:

1 N-gram model limitations Important question was asked in class: what do we do about N-grams which were not in our training corpus? Answer given: we distribute some probability mass from seen N-grams to this new N- gram. This leads to another question: how do we do this?

2 Unsmoothed bigrams Recall that we use unigram and bigram counts to compute bigram probabilities: – P(w n |w n-1 ) = C(w n-1 w n ) / C(w n-1 )

3 Recall exercise from last class Suppose text had N words, how many bigrams (tokens) does it contain? At most N: we assume appearing before first word to get a bigram probability for the word in the initial position. Example (5 words): – words: w1 w2 w3 w4 w5 – bigrams: w1, w1 w2, w2 w3, w3 w4, w4 w5

4 How many possible bigrams are there? With a vocabulary of N words, there are N 2 possible bigrams.

5 Example description Berkeley Restaurant Project corpus – approximately 10,000 sentences – 1616 word types – tables will show counts or probabilities for 7 word types, carefully chosen so that the 7 by 7 matrix is not too sparse – notice that many counts in first table are zero (25 zeros of 49 entries)

6 Unsmoothed N-grams IwanttoeatChinesefoodlunch I want to eat Chinese food lunch Bigram counts (figure 6.4 from text)

7 Computing probabilities Recall formula (we normalize by unigram counts): – P(w n |w n-1 ) = C(w n-1 w n ) / C(w n-1 ) Unigram counts are: WORDIwanttoeatChinesefoodlunch UNIGRAM COUNT p( eat | to ) = c( to eat ) / c( to ) = 860 / 3256 =.26 p( to | eat ) = c( eat to ) / c(eat) = 2 / 938 =.0021

8 Unsmoothed N-grams w n-1 w n IwanttoeatChinesefoodlunch I want to eat Chinese food lunch Bigram probabilities (figure 6.5 from text): p( w n | w n-1 )

9 What do zeros mean? Just because a bigram has a zero count or a zero probability does not mean that it cannot occur – it just means it didn’t occur in the training corpus. So we arrive back at our question: what do we do with bigrams that have zero counts when we encounter them?

10 Let’s rephrase the question How can we ensure that none of the possible bigrams have zero counts/probabilities? Process of spreading the probability mass around to all possible bigrams are called smoothing. We start with a very simple model: add-one smoothing.

11 Add-one smoothing counts New counts are gotten by adding one to original counts across the board. This ensures that there are no zero counts, but typically adds to much probability mass to non-occurring bigrams.

12 Add-one smoothing probabilities Unadjusted probabilities: – P(w n |w n-1 ) = C(w n-1 w n ) / C(w n-1 ) Adjusted probabilities: – P*(w n |w n-1 ) = [ C(w n-1 w n ) + 1 ] / [ C(w n-1 ) + V ] V is total number of word types in vocabulary In numerator we add one to the count of each bigram – as with the plain counts. In denominator we add V, since we are adding one more bigram token of the form w n-1 w, for each w in our vocabulary

13 A simple approach to smoothing: Add-one smoothing IwanttoeatChinesefoodlunch I want to eat Chinese food lunch Add-one smoothed bigram counts (figure 6.6 from text)

14 Calculating the probabilities Recall the formula for the adjusted probabilities: – P*(w n |w n-1 ) = [ C(w n-1 w n ) + 1 ] / [ C(w n-1 ) + V ] Unigram counts (adjusted by adding V=1616): WORDIwanttoeatChinesefoodlunch UNIGRAM COUNT p( eat | to ) = c( to eat ) / c( to ) = 861 / 4872 =.18 (was.26) p( to | eat ) = c( eat to ) / c( eat ) = 3 / 2554 =.0012 (was.0021) p( eat | lunch ) = c( lunch eat ) / c( lunch ) = 1 / 2075 = (was 0) p( eat | want ) = c( want eat ) / c( want ) = 1 / 2931 = (was 0)

15 A simple approach to smoothing: Add-one smoothing IwanttoeatChinesefoodlunch I want to eat Chinese food lunch Add-one smoothed bigram probabilities (figure 6.7 from text)

16 Discounting We can define the discount to be the ratio of new and old counts (in our case smoothed and unsmoothed counts). Discounts for add-one smoothing for this example: WORDIwanttoeatChinesefoodlunch ADD-ONE DISCOUNT

17 Witten-Bell discounting Another approach to smoothing Basic idea: “Use the count of things you’ve seen once to help estimate the count of things you’ve never seen.” [p. 211] Total probability mass assigned to all (as yet) unseen bigrams is T / [ T + N ], where – T is the total number of observed types – N is the number of tokens “We can think of our training corpus as a series of events; one event for each token and one event for each new type.” [p. 211] Formula above estimates “the probability of a new type event occurring.” [p. 211]

18 Distribution of probability mass This probability mass is distributed evenly amongst the unseen bigrams. Z = number of zero-count bigrams. p i * = T / [ Z*(N + T) ]

19 Discounting This probability mass has to come from somewhere! p i * = c i / (N + T) if c i > 0 Smoothed counts are c i * = T/Z * N/(N+T) if c i = 0 (work back from probability formula) c i * = c i * N/(N+T) if c i > 0

20 Witten-Bell discounting IwanttoeatChinesefoodlunch I want to eat Chinese food lunch Witten-Bell smoothed (discounted) bigram counts (figure 6.9 from text)

21 Discounting comparison Table shows discounts for add-one and Witten-Bell smoothing for this example: WORDIwanttoeatChinesefoodlunch ADD-ONE DISCOUNT WITTEN- BELL DISCOUNT

22 Training sets and test sets Corpus divided into training set and test set Need test items to not be in training set, else they will have artificially high probability Can use this to evaluate different systems: – train two different systems on the same training set – compare performance of systems on the same test set


Download ppt "N-gram model limitations Important question was asked in class: what do we do about N-grams which were not in our training corpus? Answer given: we distribute."

Similar presentations


Ads by Google