Presentation on theme: "Language Modeling: Ngrams"— Presentation transcript:
1 Language Modeling: Ngrams Julia HirschbergCS 4706
2 Approaches to Language Modeling Context-Free GrammarsUse in Pocket SphinxNgram ModelsUse for large vocabulary tasks like recognizing Broadcast News or telephone conversations
3 Problems for Larger Vocabulary Applications CFGs complicated to build and hard to modify to accommodate new data:Add capability to make a reservationAdd capability to ask for helpAdd ability to understand greetings…Parsing input with large CFGs is slow for real-time applicationsSo…for large applications we use ngram models
4 Next Word Prediction The air traffic control supervisor who admitted fallingasleep while on duty atReagan National Airport has been suspended, and thehead of the Federal Aviation Administration on Friday ordered new rules to ensure a similar incident doesn'ttake place. FAA chief Randy Babbitt said he has directed controllers at regional radar facilities to contact the towers of airports where thereis only one controller on duty at night beforesending planes on for landings. Babbitt also said regionalcontrollers have beentold that if no controller can be raised at the airport, they must offer pilots the option of diverting to anotherairport. Two commercialjets were unable to contact the controltower early Wednesday andhad to land without gaining clearance.
5 Word Prediction How do we know which words occur together? Domain knowledgeSyntactic knowledgeLexical knowledgeCan we model this knowledge computationally?Simple statistical techniques do a good job when trained appropriatelyMost common way of constraining ASR predictions to conform to probabilities of word sequences in the language – Language Modeling via N-grams
6 N-Gram Models of Language Use the previous N-1 words in a sequence to predict the next wordLanguage Model (LM)unigrams, bigrams, trigrams,…How do we train these models to discover co-occurrence probabilities?
7 Finding Corpora Corpora are online collections of text and speech Brown CorpusWall Street Journal, AP newswire, webDARPA/NIST text/speech corpora (Call Home, Call Friend, ATIS, Switchboard, Broadcast News, TDT, Communicator)Internet anywhere
8 Tokenization: Counting Words in Corpora What is a word?e.g., are cat and cats the same word? Cat and cat?September and Sept?zero and oh?Is _ a word? * ? ‘(‘ ? Uh ?Should we count parts of self-repairs? (go to fr- france)How many words are there in don’t? Gonna?Any token separated by white space from another?In Japanese, Thai, Chinese text -- how do we identify a word?
9 Terminology Sentence: unit of written language (SLU) Utterance: unit of spoken language (prosodic phrase)Wordform: inflected form as it actually appears in the corpusLemma: an abstract form, shared by word forms having the same stem, part of speech, and word sense – stands for the class of words with stem XTypes: number of distinct words in a corpus (vocabulary size)Tokens: total number of wordsStopped here
10 Simple Word Probability Assume a language has T word types and N tokens, how likely is word y to follow word x?Simplest model: 1/TBut is every word equally likely?Alternative 1: estimate likelihood of y occurring in new text based on its general frequency of occurrence estimated from a corpus (unigram probability) ct(y)/NBut is every word equally likely in every context?Alternative 2: condition the likelihood of y occurring on the context of previous words ct(x,y)/ct(x)
11 Computing Word Sequence (Sentence) Probabilities Compute probability of a word given a preceding sequenceP(the mythical unicorn…) = P(the|<start>) P(mythical|<start> the) * P(unicorn|<start> the mythical)…Joint probability: P(wn-1,wn) = P(wn | wn-1) P(wn-1)Chain Rule: Decompose joint probability, e.g. P(w1,w2,w3) asP(w1,w2, ...,wn) = P(w1) P(w2|w1) P(w3|w2,w1) … P(wn|w1 to n-1)But…the longer the sequence, the less likely we are to find it in a training corpusP(<start>Most biologists and folklore specialists believe that in fact the mythical unicorn horns derived from the narwhal<end>)
12 Bigram ModelMarkov assumption: the probability of a word depends only on the probability of a limited historyApproximate byP(unicorn|the mythical) by P(unicorn|mythical)Generalization: the probability of a word depends only on the probability of the n previous wordstrigrams, 4-grams, 5-grams…the higher n is, the more training data needed
14 What do we need: Bigram Counts eatshoneymythicalcatunicornthea<end>51028090318471512999n-1
15 Determining Bigram Probabilities Normalization: divide each row's counts by appropriate unigram counts for wn-1Computing the bigram probability of mythical mythicalC(m,m)/C(all m-initial bigrams)p (m|m) = 2 / 35 =Maximum Likelihood Estimation (MLE): relative frequency of e.g.<start>amythicalcateatshoney<end>100020035602550
16 A Simple ExampleP(a mythical cat…) = P(a | <start>) P(mythical | a) P(cat | mythical) … P(<end>|…) = 90/1000 * 5/200 * 8/35 …Needed:Bigram counts for each of these word pairs (x,y)Counts for each unigram (x) to normalizeP(y|x) = ct(x,y)/ct(x)Why do we usually represent bigram probabilities as log probabilities?What do these bigrams intuitively capture?
17 Training and Testing N-Gram probabilities come from a training corpus overly narrow corpus: probabilities don't generalizeoverly general corpus: probabilities don't reflect task or domainA separate test corpus is used to evaluate the model, typically using standard metricsheld out test set; development (dev) test setcross validationresults tested for statistical significance – how do they differ from a baseline? Other results?
18 Evaluating Ngram Models: Perplexity Information theoretic, intrinsic metric that usually correlates with extrinsic measures (e.g. ASR performance)At each choice point in a grammar or LMWeighted average branching factor: Average number of choices y following x, weighted by their probabilities of occurrenceOr, if LM(1) assigns more probability to test set sentences than LM(2), we say its perplexity is lower it models the test set better
19 Ngram PropertiesAs we increase the value of N, the accuracy of an ngram model increases – why?Ngrams are quite sensitive to the corpus they are trained onA few events (words) occur with high frequency, e.g.?Easy to collect statistics on theseA very large number occur with low frequency, e.g.?You may wait an arbitrarily long time to get valid statistics on theseSome of the zeros in a table are really zerosOthers are just low frequency events you haven't seen yetHow to allow for these events in unseen data?
20 Ngram SmoothingEvery n-gram training matrix is sparse, even for very large corporaZipf’s law: a word’s frequency is approximately inversely proportional to its rank in the word distribution listSolution:Estimate the likelihood of unseen n-gramsProblem: how do to adjust the rest of the corpus to accommodate these ‘phantom’ n-grams?Many techniques described in J&M:LaPlace or add-oneMost frequent word will occur ~twice as often as the second most frequent, three times as often as the, etc.in Brown Corpus"the" is the most frequently occurring word, and accounts for ~7% of all word occurrences (69,971 out of slightly over 1 million).second-place word "of" accounts for slightly over 3.5% of words (36,411 occurrences)followed by "and" (28,852).Only 135 vocabulary account for half the Brown Corpus.
21 Good-Turing Discounting: use counts of ngrams you’ve seen to estimate those you haven’t
22 Backoff and Interpolation For e.g. a trigram modelBackoff (e.g. Katz ‘87)Compute unigram, bigram and trigram probabilitiesIn use:Where trigram unavailable back off to bigram if available, o.w. unigram probabilityE.g An omnivorous unicornInterpolationCompute trigram by linearly interpolating trigram, bigram, and unigram models, weighted appropriately
23 LM toolkits The CMU-Cambridge LM toolkit (CMULM) The SRILM toolkit The SRILM toolkit