jurafsky&martin_3rdEd_17 (1).pdf

# Consider the job of predicting the next word in this

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Consider the job of predicting the next word in this sentence, assuming we are inter- polating a bigram and a unigram model. I can’t see without my reading . The word glasses seems much more likely to follow here than, say, the word Kong , so we’d like our unigram model to prefer glasses . But in fact it’s Kong that is more common, since Hong Kong is a very frequent word. A standard unigram model will assign Kong a higher probability than glasses . We would like to capture the intuition that although Kong is frequent, it is mainly only frequent in the phrase Hong Kong , that is, after the word Hong . The word glasses has a much wider distribution. In other words, instead of P ( w ) , which answers the question “How likely is w ?”, we’d like to create a unigram model that we might call P CONTINUATION , which answers the question “How likely is w to appear as a novel continuation?”. How can we estimate this probability of seeing the word w as a novel continuation, in a new unseen context? The Kneser-Ney intuition is to base our estimate of P CONTINUATION on the number of different contexts word w has appeared in , that is, the number of bigram types it completes. Every bigram type was a novel continuation the first time it was seen. We hypothesize that words that have appeared in more contexts in the past are more likely to appear in some new context as well. The number of times a word w appears as a novel continuation can be expressed as: P CONTINUATION ( w ) µ |{ v : C ( vw ) > 0 }| (4.29) To turn this count into a probability, we normalize by the total number of word bigram types. In summary: P CONTINUATION ( w ) = |{ v : C ( vw ) > 0 }| |{ ( u 0 , w 0 ) : C ( u 0 w 0 ) > 0 }| (4.30) An alternative metaphor for an equivalent formulation is to use the number of word types seen to precede w (Eq. 4.29 repeated): P CONTINUATION ( w ) µ |{ v : C ( vw ) > 0 }| (4.31) normalized by the number of words preceding all words, as follows: P CONTINUATION ( w ) = |{ v : C ( vw ) > 0 }| P w 0 |{ v : C ( vw 0 ) > 0 }| (4.32) A frequent word (Kong) occurring in only one context (Hong) will have a low continuation probability. The final equation for Interpolated Kneser-Ney smoothing for bigrams is then: Interpolated Kneser-Ney P KN ( w i | w i - 1 ) = max ( C ( w i - 1 w i ) - d , 0 ) C ( w i - 1 ) + l ( w i - 1 ) P CONTINUATION ( w i ) (4.33) The l is a normalizing constant that is used to distribute the probability mass we’ve discounted.: l ( w i - 1 ) = d P v C ( w i - 1 v ) |{ w : C ( w i - 1 w ) > 0 }| (4.34)

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4.6 T HE W EB AND S TUPID B ACKOFF 53 The first term d P v C ( w i - 1 v ) is the normalized discount. The second term |{ w : C ( w i - 1 w ) > 0 }| is the number of word types that can follow w i - 1 or, equivalently, the number of word types that we discounted; in other words, the number of times we applied the normalized discount. The general recursive formulation is as follows: P KN ( w i | w i - 1 i - n + 1 ) = max ( c KN ( w i i - n + 1 ) - d , 0 ) P v c KN ( w i - 1 i - n + 1 v ) + l ( w i - 1 i - n + 1 ) P KN ( w i | w i - 1 i - n + 2 ) (4.35) where the definition of the count c KN depends on whether we are counting the highest-order N-gram being interpolated (for example trigram if we are interpolat- ing trigram, bigram, and unigram) or one of the lower-order N-grams (bigram or
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