1. 1 SIMS 290-2: Applied Natural Language Processing Marti Hearst Sept 15, 2004

2. 2 Class Pace and Schedule Need a foundation before you can do anything interesting. Tokenizing, Tagging, Regex’s Text Classification Principles and Techniques Training vs. Testing, processing corpora Through (approximately) the 6 th week, keep doing exercises from the NLTK tutorials to build that foundation. 2 more homeworks I’m trying to make them bite-sized pieces 7 th – 10 th Group Miniproject on Enron Corpus Will involve classification or Information Extraction Different groups will do different things May have a homework within this timeframe 11 th – 15 th Another Miniproject Either on Enron project or your choices I will suggest ideas; you can propose them too May also have 1-2 other homeworks in this timeframe

3. 3 Language Modeling An fundamental concept in NLP Main idea: For a given language, some words are more likely than others to follow each other, or You can predict (with some degree of accuracy) the probability that a given word will follow another word. Illustration: Distributions of words in class-participation exercise.

4. 4 Adapted from slide by Bonnie Dorr Next Word Prediction From a NY Times story... Stocks... Stocks plunged this …. Stocks plunged this morning, despite a cut in interest rates Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall... Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began

5. 5 Adapted from slide by Bonnie Dorr Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began trading for the first time since last … Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began trading for the first time since last Tuesday's terrorist attacks.

6. 6 Adapted from slide by Bonnie Dorr Human Word Prediction Clearly, at least some of us have the ability to predict future words in an utterance. How? Domain knowledge Syntactic knowledge Lexical knowledge

7. 7 Adapted from slide by Bonnie Dorr Claim A useful part of the knowledge needed to allow word prediction can be captured using simple statistical techniques In particular, we'll rely on the notion of the probability of a sequence (a phrase, a sentence)

8. 8 Adapted from slide by Bonnie Dorr Applications Why do we want to predict a word, given some preceding words? Rank the likelihood of sequences containing various alternative hypotheses, e.g. for ASR Theatre owners say popcorn/unicorn sales have doubled... Assess the likelihood/goodness of a sentence –for text generation or machine translation. The doctor recommended a cat scan. El doctor recommendó una exploración del gato.

9. 9 Adapted from slide by Bonnie Dorr N-Gram Models of Language Use the previous N-1 words in a sequence to predict the next word Language Model (LM) unigrams, bigrams, trigrams,… How do we train these models? Very large corpora

10. 10 Adapted from slide by Bonnie Dorr Simple N-Grams Assume a language has V word types in its lexicon, how likely is word x to follow word y? Simplest model of word probability: 1/V Alternative 1: estimate likelihood of x occurring in new text based on its general frequency of occurrence estimated from a corpus (unigram probability) popcorn is more likely to occur than unicorn Alternative 2: condition the likelihood of x occurring in the context of previous words (bigrams, trigrams,…) mythical unicorn is more likely than mythical popcorn

11. 11 A Word on Notation P(unicorn) Read this as “The probability of seeing the token unicorn” Unigram tagger uses this. P(unicorn|mythical) Called the Conditional Probability. Read this as “The probability of seeing the token unicorn given that you’ve seen the token mythical Bigram tagger uses this. Related to the conditional frequency distributions that we’ve been working with.

12. 12 Adapted from slide by Bonnie Dorr Computing the Probability of a Word Sequence Compute the product of component conditional probabilities? P(the mythical unicorn) = P(the) P(mythical|the) P(unicorn|the mythical) The longer the sequence, the less likely we are to find it in a training corpus P(Most biologists and folklore specialists believe that in fact the mythical unicorn horns derived from the narwhal) Solution: approximate using n-grams

13. 13 Adapted from slide by Bonnie Dorr Bigram Model Approximate by P(unicorn|the mythical) by P(unicorn|mythical) Markov assumption: The probability of a word depends only on the probability of a limited history Generalization: The probability of a word depends only on the probability of the n previous words –trigrams, 4-grams, … –the higher n is, the more data needed to train –backoff models

14. 14 Adapted from slide by Bonnie Dorr Using N-Grams For N-gram models P(w n-1,w n ) = P(w n | w n-1 ) P(w n-1 ) By the Chain Rule we can decompose a joint probability, e.g. P(w 1,w 2,w 3 ) P(w 1,w 2,...,w n ) = P(w 1 |w 2,w 3,...,w n ) P(w 2 |w 3,...,w n ) … P(w n-1 |w n ) P(w n ) For bigrams then, the probability of a sequence is just the product of the conditional probabilities of its bigrams P(the,mythical,unicorn) = P(unicorn|mythical)P(mythical|the) P(the| )

15. 15 Adapted from slide by Bonnie Dorr Training and Testing N-Gram probabilities come from a training corpus overly narrow corpus: probabilities don't generalize overly general corpus: probabilities don't reflect task or domain A separate test corpus is used to evaluate the model, typically using standard metrics held out test set; development test set cross validation results tested for statistical significance

16. 16 Adapted from slide by Bonnie Dorr A Simple Example From BeRP: The Berkeley Restaurant Project A testbed for a Speech Recognition project System prompts user for information in order to fill in slots in a restaurant database. –Type of food, hours open, how expensive After getting lots of input, can compute how likely it is that someone will say X given that they already said Y. P(I want to each Chinese food) = P(I | ) P(want | I) P(to | want) P(eat | to) P(Chinese | eat) P(food | Chinese)

17. 17 Adapted from slide by Bonnie Dorr A Bigram Grammar Fragment from BeRP.001Eat British.03Eat today.007Eat dessert.04Eat Indian.01Eat tomorrow.04Eat a.02Eat Mexican.04Eat at.02Eat Chinese.05Eat dinner.02Eat in.06Eat lunch.03Eat breakfast.06Eat some.03Eat Thai.16Eat on

18. 18 Adapted from slide by Bonnie Dorr.01British lunch.05Want a.01British cuisine.65Want to.15British restaurant.04I have.60British food.08I don’t.02To be.29I would.09To spend.32I want.14To have.02 I’m.26To eat.04 Tell.01Want Thai.06 I’d.04Want some.25 I

19. 19 Adapted from slide by Bonnie Dorr P(I want to eat British food) = P(I| ) P(want|I) P(to|want) P(eat|to) P(British|eat) P(food|British) =.25*.32*.65*.26*.001*.60 =.000080 vs. I want to eat Chinese food =.00015 Probabilities seem to capture “syntactic'' facts, “world knowledge'' eat is often followed by an NP British food is not too popular N-gram models can be trained by counting and normalization

20. 20 Adapted from slide by Bonnie Dorr What do we learn about the language? What's being captured with... P(want | I) =.32 P(to | want) =.65 P(eat | to) =.26 P(food | Chinese) =.56 P(lunch | eat) =.055 What about... P(I | I) =.0023 P(I | want) =.0025 P(I | food) =.013

21. 21 Modified from Massio Poesio's lecture Tagging with lexical frequencies Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN Problem: assign a tag to race given its lexical frequency Solution: we choose the tag that has the greater P(race|VB) Probability of “race” given “VB” on prior word P(race|NN) Probability of “race” given “NN” on prior word Actual estimate from the Switchboard corpus: P(race|NN) =.00041 P(race|VB) =.00003

22. 22 Modified from Diane Litman's version of Steve Bird's notes Combining Taggers Use more accurate algorithms when we can, backoff to wider coverage when needed. Try tagging the token with the 1st order tagger. If the 1st order tagger is unable to find a tag for the token, try finding a tag with the 0th order tagger. If the 0th order tagger is also unable to find a tag, use the NN_CD_Tagger to find a tag.

23. 23 Modified from Diane Litman's version of Steve Bird's notes BackoffTagger class >>> train_toks = TaggedTokenizer().tokenize(tagged_text_str) # Construct the taggers >>> tagger1 = NthOrderTagger(1, SUBTOKENS=‘WORDS’) >>> tagger2 = UnigramTagger() # 0th order >>> tagger3 = NN_CD_Tagger() # Train the taggers >>> for tok in train_toks: tagger1.train(tok) tagger2.train(tok)

24. 24 Modified from Diane Litman's version of Steve Bird's notes Backoff (continued) # Combine the taggers (in order, by specificity) > tagger = BackoffTagger([tagger1, tagger2, tagger3]) # Use the combined tagger > accuracy = tagger_accuracy(tagger, unseen_tokens)

25. 25 Modified from Diane Litman's version of Steve Bird's notes Rule-Based Tagger The Linguistic Complaint Where is the linguistic knowledge of a tagger? Just a massive table of numbers Aren’t there any linguistic insights that could emerge from the data? Could thus use handcrafted sets of rules to tag input sentences, for example, if input follows a determiner tag it as a noun.

26. 26 Slide modified from Massimo Poesio's The Brill tagger An example of TRANSFORMATION-BASED LEARNING Very popular (freely available, works fairly well) A SUPERVISED method: requires a tagged corpus Basic idea: do a quick job first (using frequency), then revise it using contextual rules

27. 27 Brill Tagging: In more detail Start with simple (less accurate) rules…learn better ones from tagged corpus Tag each word initially with most likely POS Examine set of transformations to see which improves tagging decisions compared to tagged corpus Re-tag corpus using best transformation Repeat until, e.g., performance doesn’t improve Result: tagging procedure (ordered list of transformations) which can be applied to new, untagged text

28. 28 Slide modified from Massimo Poesio's An example Examples: They are expected to race tomorrow. The race for outer space. Tagging algorithm: 1.Tag all uses of “race” as NN (most likely tag in the Brown corpus) They are expected to race/NN tomorrow the race/NN for outer space 2.Use a transformation rule to replace the tag NN with VB for all uses of “race” preceded by the tag TO: They are expected to race/VB tomorrow the race/NN for outer space

29. 29 First 20 Transformation Rules From: Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging Eric Brill. Computational Linguistics. December, 1995.

30. 30 Transformation Rules for Tagging Unknown Words From: Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging Eric Brill. Computational Linguistics. December, 1995.

31. 31 Adapted from Massio Peosio's Additional issues Most of the difference in performance between POS algorithms depends on their treatment of UNKNOWN WORDS Class-based N-grams

32. 32 Modified from Diane Litman's version of Steve Bird's notes Evaluating a Tagger Tagged tokens – the original data Untag (exclude) the data Tag the data with your own tagger Compare the original and new tags Iterate over the two lists checking for identity and counting Accuracy = fraction correct

33. 33 Assessing the Errors Why the tuple method? Dictionaries cannot be indexed by lists, so convert lists to tuples. exclude returns a new token containing only the properties that are not named in the given list.

34. 34 Assessing the Errors

35. 35 Upcoming First assignment due 8pm tonight Turn in on course Assignments page For next week: Read the Chunking tutorial. (The pdf version has the missing images) http://nltk.sourceforge.net/tutorial/chunking.pdf We’ll have an assignment getting practice with this.