1. CS626-449: Speech, NLP and the Web/Topics in AI Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture-14: Probabilistic parsing; sequence labeling, PCFG

2. Recap: Two Views of NLP 1.Classical View: Layered Procssing;Various Ambiguities (already discussed) 2.Statistical/Machine Learning View

3. What is the output of an ML-NLP System (1/2) Option 1: A set of rules, e.g., –If the word to the left of the verb is a noun and has animacy feature, then it is the likely agent of the action denoted by the verb. The child broke the toy (child is the agent) The window broke (window is not the agent; inanimate)

4. What is the output of an ML-NLP System (2/2) Option 2: a set of probability values –P(agent|word is to the left of verb and has animacy) > P(object|word is to the left of verb and has animacy)> P(instrument|word is to the left of verb and has animacy) etc.

5. How is this different from classical NLP corpus Text data Linguist Computer rules rules/probabilities Classical NLP Statistical NLP

6. Classification appears as sequence labeling

7. A set of Sequence Labeling Tasks: smaller to larger units Words: –Part of Speech tagging –Named Entity tagging –Sense marking Phrases: Chunking Sentences: Parsing Paragraphs: Co-reference annotating

8. Example of word labeling: POS Tagging Come September, and the UJF campus is abuzz with new and returning students. Come_VB September_NNP,_, and_CC the_DT UJF_NNP campus_NN is_VBZ abuzz_JJ with_IN new_JJ and_CC returning_VBG students_NNS._.

9. Example of word labeling: Named Entity Tagging September UJF

10. Example of word labeling: Sense Marking WordSynsetWN-synset-no come{arrive, get, come} 01947900. abuzz{abuzz, buzzing, droning}01859419

11. Example of phrase labeling: Chunking Come July, and is abuzz with. the UJF campus new and returning students

12. Example of Sentence labeling: Parsing [ S1 [ S [ S [ VP [ VB Come][ NP [ NNP July]]]] [,,] [ CC and] [ S [ NP [ DT the] [ JJ UJF] [ NN campus]] [ VP [ AUX is] [ ADJP [ JJ abuzz] [ PP [ IN with] [ NP [ ADJP [ JJ new] [ CC and] [ VBG returning]] [ NNS students]]]]]] [..]]]

13. Handling labeling through the Noisy Channel Model w t (w n, w n-1, …, w 1 ) (t m, t m-1, …, t 1 ) Noisy Channel Sequence w is transformed into sequence t.

14. Bayesian Decision Theory and Noisy Channel Model are close to each other Bayes Theorem : Given the random variables A and B, Posterior probability Prior probability Likelihood

15. Corpus A collection of text called corpus, is used for collecting various language data With annotation: more information, but manual labor intensive Practice: label automatically; correct manually The famous Brown Corpus contains 1 million tagged words. Switchboard: very famous corpora 2400 conversations, 543 speakers, many US dialects, annotated with orthography and phonetics

16. Discriminative vs. Generative Model W * = argmax (P(W|SS)) W Compute directly from P(W|SS) Compute from P(W).P(SS|W) Discriminative Model Generative Model

17. Notion of Language Models

18. Language Models N-grams: sequence of n consecutive words/chracters Probabilistic / Stochastic Context Free Grammars:  Simple probabilistic models capable of handling recursion  A CFG with probabilities attached to rules  Rule probabilities  how likely is it that a particular rewrite rule is used?

19. PCFGs Why PCFGs?  Intuitive probabilistic models for tree-structured languages  Algorithms are extensions of HMM algorithms  Better than the n-gram model for language modeling.

20. Formal Definition of PCFG A PCFG consists of  A set of terminals {w k }, k = 1,….,V {w k } = { child, teddy, bear, played…}  A set of non-terminals {N i }, i = 1,…,n {N i } = { NP, VP, DT…}  A designated start symbol N 1  A set of rules {N i   j }, where  j is a sequence of terminals & non-terminals NP  DT NN  A corresponding set of rule probabilities

21. Rule Probabilities  Rule probabilities are such that E.g., P( NP  DT NN) = 0.2 P( NP  NN) = 0.5 P( NP  NP PP) = 0.3  P( NP  DT NN) = 0.2  Means 20 % of the training data parses use the rule NP  DT NN