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Chapter 4: N-GRAMS

Chapter 4: N-GRAMS

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Chapter 4: N-GRAMS

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  1. Chapter 4: N-GRAMS Heshaam Faili University of Tehran

  2. N-Gram • But it must be recognized that the notion “probability of a sentence” is an entirely useless one, under any known interpretation of this term. Noam Chomsky (1969, p. 57) • Anytime a linguist leaves the group the recognition rate goes up. Fred Jelinek (then of the IBM speech group) (1988)

  3. N-grams: Motivation • An n-gram is a stretch of text n words long • Approximation of language: information in n-grams tells us something about language, but doesn’t capture the structure • Efficient: finding and using every, e.g., two-word collocation in a text is quick and easy to do • N-grams can help in a variety of NLP applications: • Word prediction = n-grams can be used to aid in predicting the next word of an utterance, based on the previous n- 1 words • Useful for context-sensitive spelling correction, approximation of language, ...

  4. Corpus-based NLP • Corpus (pl. corpora) = a computer-readable collection of text and/or speech, often with annotations • We can use corpora to gather probabilities and other information about language us • We can say that a corpus used to gather prior information is training data • Testing data, by contrast, is the data one uses to test the accuracy of a method • We can distinguish types and tokens in a corpus • type = distinct word (e.g., like) • token = distinct occurrence of a word (e.g., the type like might have 20,000 tokens in a corpus)

  5. Corpora • Brown: Brown corpus is a 1 million word collection of samples from 500 written texts from different genres • Switchboard: corpus of telephone conversations between strangers was collected in the early 1990s and contains 2430 conversations averaging 6 minutes each, totaling 240 hours of speech and about 3 million words

  6. Different challenges in corpora • Punctuation? • Utterance (Uh, Um, …)? • Case Sensitive? • Inflected word? • Lemma: set of lexical form having same stem, same POS tag, same sense • In rich morphological languages, like Arabic, Persian, need to deal with lemmatization but in English it’s easier to deal with wordforms

  7. Simple n-grams • Let’s assume we want to predict the next word, based on the previous context of I dreamed I saw the knights inWhat we want to find is the likelihood of w8 being the next word, given that we’ve seen w1, ..., w7, in other words,P(w1, ..., w8) • In general, for wn, we are looking for:(1) P(w1, ..., wn) = P(w1)P(w2|w1)...P(wn|w1, ..., wn-1) • But these probabilities are impractical to calculate: they hardly ever occur in a corpus, if at all. (And it would be a lot of data to store, if we could calculate them.)

  8. Unigrams • So, we can approximate these probabilities to a particular n-gram, for a given n. What should n be? • Unigrams (n= 1):(2) P(wn|w1, ..., wn-1) ≈ P(wn) • Easy to calculate, but we have no contextual information(3) The quick brown fox jumped • We would like to say that over has a higher probability in this context than lazy does.

  9. Bigrams • bigrams (n= 2) are a better choice and still easy to calculate:(4) P(wn|w1, ..., wn-1) ≈ P(wn|wn-1)(5) P( over| The, quick, brown, fox, jumped) ≈ P( over| jumped) • And thus, we obtain for the probability of a sentence:(6) P(w1, ..., wn) = P(w1)P(w2|w1)P(w3|w2)...P(wn|wn-1)

  10. Markov models • A bigram model is also called a first-order Markov model because it has one element of memory (one token in the past) • Markov models are the class of probabilistic models that assume that we can predict the probability of some future unit without looking too far into the past. • Markov models are essentially weighted FSAs—i.e., the arcs between states have probabilities • The states in the FSA are words • Much more on Markov models when we hit POS tagging ...

  11. Bigram example • What is the probability of seeing the sentence The quick brown fox jumped over the lazy dog? • (7) P(The quick brown fox jumped over the lazy dog) =P( The| < start >) P( quick| The)P( brown| quick)...P( dog| lazy) • Probabilities are generally small, so log probabilities are usually used • Does this favor shorter sentences?

  12. Trigrams • If bigrams are good, then trigrams ( n= 3) can be even better. • Wider context: P( know| did, he) vs. P( know| he) • Generally, trigrams are still short enough that we will have enough data to gather accurate probabilities

  13. Training n-gram models • Go through corpus and calculate relative frequencies: • (8) P(wn|wn-1) = C(wn-1,wn) / C(wn-1) • (9) P(wn|wn-2, wn-1) = C(wn-2,wn-1,wn) / C(wn-2,wn-1) • This technique of gathering probabilities from a training corpus is called maximum likelihood estimation (MLE)

  14. Maximum likelihood estimation (MLE) • In MLE, the resulting parameter set maximizes the likelihood of the training set T given the modelM (i.e., P(T|M)). • Example: Chinese occurs 400 times in 1 million words Brown corpus. That is MLE = 0.0004 . • 0.0004 is not the best possible estimate of the probability of Chinese occurring in all situation, But it is the probability that makes it most likely that Chinese will occur 400 times in a million-word corpus

  15. Know your corpus • We mentioned earlier about having training data and testing data ... it’s important to remember what your training data is when applying your technology to new data • If you train your trigram model on Shakespeare, then you have learned the probabilities in Shakespeare, not the probabilities of English overall • What corpus you use depends on what you want to do later

  16. BerkeleyRestaurant Project

  17. BerkeleyRestaurant Project

  18. WSJ trained

  19. Open versus closed vocabulary tasks • closed vocabulary assumption • Unknown word(out of vocabulary) • OOV rate • Should to model OOV • Choose a vocabulary (word list) which is fixed in advance. • Convert in the training set any word that is not in this set (any OOV word) to the unknown word token <UNK> in a text normalization step. • Estimate the probabilities for <UNK> from its counts just like any other regular word in the training set.

  20. Evaluating N-gram • extrinsic evaluation: embed the model in an application and measure the total performance of the application • Very expensive • intrinsitic evaluation metric is one which measures the quality of a model independent of any application • Perplexity measure

  21. Perplexity • given two probabilistic models, the better model is the one that has a tighter fit to the test data • the probability the model assigns to the test data; the better model will assign a higher probability to the test data • W=w1w2…wn

  22. Perplexity

  23. Perplexity • weighted average branching factor: the number of possible next words that can follow any word • Consider the task of recognizing the digits in English (zero, one, two,..., nine), given that each of the 10 digits occur with equal probability P = 1/10 . The perplexity of this mini-language is in fact 10.

  24. Perplexity example • WSJ

  25. Smoothing: Motivation • Let’s assume that we have a good corpus and have trained a bigram model on it, i.e., learned MLE probabilities for bigrams • But we won’t have seen every possible bigramlickety split is a possible English bigram, but it may not be in the corpus • This is a problem of data sparseness  there are zero probability bigrams which are actual possible bigrams in the language • To account for this sparseness, we turn to smoothing techniques  making zero probabilities non-zero, i.e., adjusting probabilities to account for unseen data

  26. Add-One (Laplace) Smoothing • One way to smooth is to add a count of one to every bigram: • in order to still be a probability, all probabilities need to sum to one • so, we add the number of word types to the denominator (i.e., we added one to every type of bigram, so we need to account for all our numerator additions) • (10) P(wn|wn-1) = ( C(wn-1,wn)+1 ) /( C(wn-1)+V ) • V = total number of word types that we might see

  27. Add-One smoothing • P(Wx) = C(Wx) / iC(Wi) • Adjusted Count • C*i = (Ci+1)(N/(N+V)) • Discount • dc = c*/c • P*i = (ci+1)/(N+V) • P(Wn|Wn-1)=(C(Wn-1Wn)+1)/(C(Wn-1)+V)

  28. Smoothing example • So, if treasure trove never occurred in the data, but treasure occurred twice, we have: • (11) P(trove | treasure) = (0+1)/(2+V) • The probability won’t be very high, but it will be better than 0 • If all the surrounding probabilities are still high, then treasure trove could still be the best pick • If the probability were zero, there would be no chance of it appearing.

  29. Berkeley Restaurant project

  30. C(want to) : 608 to 238 • P(to | want) : 0.66 to 0.26 • Large changes ! Need to be more accurate …

  31. Discounting • An alternate way of viewing smoothing is as discounting • Lowering non-zero counts to get the probability mass we need for the zero count items • The discounting factor can be defined as the ratio of the smoothed count to the MLE count

  32. Witten-Bell Discounting • Use the count of things you’ve seen once to help estimate the count of things you’ve never seen • Probability of seeing an N-gram for the first time: counting the number of times we saw N-grams for the first time in the training corpus • i:ci=0 pi* = T/(T+N) • The above value is the total probability, should be divided by total number that is Z=i:ci=0 1 • P*i=T/Z(T+N)

  33. Witten-Bell Discounting • Pi* = ci/(N+T) if(ci>0) • ci* = T/Z . N/(N+T) if(ci = 0) • ci* = ci . N/(N+T) if(ci > 0)

  34. Witten-Bell Discounting, Bigram • Main idea: Instead of simply adding one to every n-gram, compute the probability of wi-1, wi by seeing how likely wi-1 is at starting any bigram. • Words that begin lots of bigrams lead to higher “unseen bigram” probabilities • Non-zero bigrams are discounted in essentially the same manner as zero count bigrams

  35. Witten-Bell Discounting formula • (12) zero count bigrams: p*(wi|wi-1) =T(wi-1)/ (Z(wi-1) ( N(wi-1)+T(wi-1)) ) • T(wi-1) = the number of bigram types starting with wi-1 • N(wi-1) = the number of bigram tokens starting with wi-1 • N(wi-1) + T(wi-1) gives us the total number of “events” to divide by • Z(wi-1) = the number of bigram tokens starting with wi-1 and having zero count • this just distributes the probability mass between all zero count bigrams starting with wi-1

  36. Good Turing discouting • Using Joint Probability instead of conditional probability i.e. P(wxwi) instead of p(wi|wx) • The main idea is to re-estimate the amount of probability mass to assign to N-grams with zero or low counts by looking at the number of N-grams with higher counts.

  37. Good Turing discouting • Nc : number of n-grams that occurs c times • Nc =b:c(b)=c 1 • Good-Turing estimate: ci*=(ci+1)Nc+1/Nc • For c=0, c* = N1/N0 • But in real, c* = c for c>k

  38. Example • Suppose we are fishing in a lake with 8 species (bass, carp, catfish, eel, perch, salmon, trout, whitefish) and we have seen 6 species with the following counts: 10 carp, 3 perch, 2 whitefish, 1 trout, 1 salmon, and 1 eel (so we haven’t yet seen the catfish or bass)

  39. Another example

  40. Better estimation • Discounting can help to solve the problem of zero frequency N-grams • But it’s not the only knowledge to be used • If the tri-gram probability is zero use bi-gram probability • If the bi-gram probability is zero use uni-gram probability • Backoff • Interpolation

  41. Backoff models: Basic idea • Let’s say we’re using a trigram model for predicting language, and we haven’t seen a particular trigram before. • But maybe we’ve seen the bigram, or if not, the unigram information would be useful • Backoff models allow one to try the most informative n-gram first and then back off to lower n-grams

  42. Backoff equations • Roughly speaking, this is how a backoff model works: • If this trigram has a non-zero count, we use that information(13) Pˆ(wi|wi-2 wi-1) = P(wi|wi-2 wi-1) • else, if the bigram count is non-zero, we use that bigram information:(14) Pˆ(wi|wi-2 wi-1) = 1 P(wi|wi-1) • and in all other cases we just take the unigram information:(15) Pˆ(wi|wi-2 wi-1) = 2 P(wi)

  43. Backoff models: example • Let’s say we’ve never seen the trigram “maples want more” before • But we have seen “want more”, so we can use that bigram to calculate a probability estimate. • So, we look at P(more|want) ... • But we’re now assigning probability toP(more|maples, want) which was zero before  we won’t have a true probability model anymore • This is why 1 was used in the previous equations, to assign less re-weight to the probability. • In general, backoff models have to be combined with discounting models

  44. Deleted (simple) Interpolation • Deleted interpolation is similar to backing off, except that we always use the bigram and unigram information to calculate the probability estimate • Every trigram probability, then, is a composite of the focus word’s trigram, bigram, and unigram.

  45. Context-conditioned Interpolation • s trained on another training corpus named held-out corpus • Train the N-gram with main training corpus and in the fixed n-gram model, trained the  with held-out corpus

  46. Class-based N-grams • Use information about the word classes (clusters) instead of words • Instead of using “to Shanghai” use “to London”, “to Beijing”, … , “to CITIES” • IBM Clustering • P(wi|Wi-1)  P(Ci|Ci-1) . P(wi|Ci)

  47. Language Model Adaptation and Using the Web • Language model adaptation: train on the larger out-of-domain for some other domain data.

  48. Information Theory • Another view of Perplexity is Information theory and cross entropy • Entropy is a measure of information, ENTROPY and is invaluable throughout speech and language processing • It can be used as a metric for how much information there is in a particular grammar, for how well a given grammar matches a given language, for how predictive a given N-gram grammar is about what the next word could be. • to compare how difficult two speech recognition tasks are, and also to measure how well a given probabilistic grammar matches human grammars

  49. Information Theory • as a lower bound on the number of bits it would take to encode a certain decision or piece of information in the optimal coding scheme