CS 562: STATISTICAL NATURAL LANGUAGE PROCESSING

Transcription

1 CS 562: STATISTICAL NATURAL LANGUAGE PROCESSING August 2010 Instructors: Liang Huang and Kevin Knight TA: Jason Riesa

2 Doesn t Google know everything? What animal does a cat eat? 2

3 Even Key Word Queries Paris Hilton -- not easy to book! (vs. Boston Hilton) 3

4 Ambiguity Where can I spot a snow leopard? 4

5 More about Ambiguities to middle school kids: what does this sentence mean? I saw her duck. Aravind Joshi 5

6 More about Ambiguities to middle school kids: what does this sentence mean? I saw her duck. Aravind Joshi 5

7 More about Ambiguities to middle school kids: what does this sentence mean? I saw her duck. Aravind Joshi lexical ambiguity (word-sense) 5

8 More about Ambiguities to middle school kids: what does this sentence mean? I eat sushi with tuna. Aravind Joshi 6

9 More about Ambiguities to middle school kids: what does this sentence mean? I eat sushi with tuna. Aravind Joshi structural ambiguity (PP-attachment) 6

10 More about Ambiguities to middle school kids: what does this sentence mean? I eat sushi with tuna. Aravind Joshi 7

11 More about Ambiguities to middle school kids: what does this sentence mean? I eat sushi with tuna. Aravind Joshi lexical ambiguity (word-sense) 7

12 More about Ambiguities to middle school kids: what does this sentence mean? Everybody loves somebody. Aravind Joshi??? 8

13 More about Ambiguities to middle school kids: what does this sentence mean? Everybody loves somebody. Aravind Joshi??? structural ambiguity (quantifier scope) 8

14 More about Ambiguities to middle school kids: what does this sentence mean? Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo Aravind Joshi 9

15 More about Ambiguities to middle school kids: what does this sentence mean? Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo Aravind Joshi 9

16 More about Ambiguities to middle school kids: what does this sentence mean? Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo Aravind Joshi 9

17 More about Ambiguities to middle school kids: what does this sentence mean? Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo Aravind Joshi Dogs dogs dog dog dogs. Police police police police police 9

18 Ambiguities in Translation zi zhu zhong duan self help terminal device 10

19 Ambiguities in Translation 11

20 Ambiguities in Translation 11

21 Ambiguities in Translation 11

22 Ambiguities in Translation 11

23 If you are stolen... 12

24 or even... 13

25 or even... clear evidence that NLP is used in real life! 13

26 Grammar SBARQ WHNP SINV What animal VBZ NP VP does a cat VB NP eat t 14

27 PP Attachment Ambiguity DP for incremental parsing 15

28 PP Attachment Ambiguity One morning in Africa, I shot an elephant in my pajamas; DP for incremental parsing 15

29 PP Attachment Ambiguity One morning in Africa, I shot an elephant in my pajamas; how he got into my pajamas I ll never know. DP for incremental parsing 15

30 PP Attachment Ambiguity One morning in Africa, I shot an elephant in my pajamas; how he got into my pajamas I ll never know. DP for incremental parsing 15

31 Ambiguity Explosion I saw her duck. 16

32 Ambiguity Explosion I saw her duck. how about... I saw her duck with a telescope.... I saw her duck with a telescope in the garden... 16

33 Ambiguity Explosion exponential explosion of the search space Q1: how to represent ambiguities (compactly)? Q2: how to search over this space (efficiently)? Q3: how to rank different hypotheses? S NP VP PRP VBD NP PP I saw PRP$ NN IN NP.. her duck with DT a NN telescope 17

34 Answers... Q1: how to represent ambiguities? context-free grammar (unit 2) finite-state automata (unit I) Q2: how to search in this space? dynamic programming (units 1&2) Q3: how to rank these hypotheses? weighted grammar (units 1-3) weights learned from data NP PRP I VBD saw S VP NP PRP$ NN her duck IN with PP DT a NP NN telesco (saw, with, telescope) seen more often in texts 18

35 Why Learning? learning is better than hand-written rules, because: less work; easily adapts to new languages/domains Powerset (now bing.com): 15 years for English grammar! now they are writing their Chinese grammar... and languages constantly change! learning can work, and often works better! machine translation: used to be dominated by rule-based now statistical methods are better: google vs. systran google learns from the web, and translates 40+ langs [also CS 567, Machine Learning, Fall 2010] 19

36 Example - Rosetta Stone the most famous (tri-)parallel text machines can do the same job! (if given parallel text) UN/EU/Ca proceedings, News, tech manuals,... 20

37 Take Home Message languages are beyond just bags of words! ambiguity is everywhere, and NLP is all about that we ll teach machines how to read and translate... and how to learn to read and translate from data have fun in this class! :) 21