CSC 594 Topics in AI – Applied Natural Language Processing

1. CSC 594 Topics in AI –Applied Natural Language Processing Fall 2009/2010 1. Introduction

2. What is NLP? • Natural Language Processing (NLP) is a field in Artificial Intelligence (AI) devoted to creating computers that use natural language as input and/or output.

3. NLP involves other disciplines.. • Linguistics • NLP is also called ”Computational Linguistics” • Psychology • Mathematics and Statistics • Information Theory • Computer Science

4. Machines that Can Speak • HAL 9000 in “2001: A Space Odyssey”

5. Machines that Can Speak (cont.) • KITT in Knight Rider • C3PO in Star Wars

6. But Still a Sci-Fi… • “However, as 2001 approached it became clear that 2001's predictions in computer technology were far fetched. Natural language, lip reading, planning and plain common sense in computers were still the stuff of science fiction.”HAL 9000 - Wikipedia

7. NLP is AI-complete “The most difficult problems in AI manifest themselves in human language phenomena.” Use of language is the touchstone of intelligent behavior.

8. Test for Intelligence – Turing Test Alan Turing (1950) proposed a test of a machine's capability to perform human-like conversation. • A human judge engages in a natural language conversation with two other parties, one a human and the other a machine; if the judge cannot reliably tell which is which, then the machine is said to pass the test.

9. Early Conversational Programs ELIZA(by Joseph Weizenbaum), 1966 A psychotherapist No real understanding; simple pattern-matching to respond to user input -> canned responses (my ?x depresses me) (why does your ?x depress you) 5 (life ?x) (why do you say it ?x) 3 (I could ?x) (you could ?x) 2 (because ?x) (that is a good reason) 3 (?x) (tell me more) 0

10. >> Hello. How do you do. Please state your problem. >> Men are all alike. In what way ? >> They're alwas bugging us about something or other. I'm not sure I understand you fully. >> They're always bugging us about something or other. Can you think of a specific example ? >> Well, my boyfriend made me come here. Your boyfriend made you come here ? >> He says I'm depressed much of the time. I am sorry to hear that you are depressed.

11. Recent Conversational ProgramsLoebner Prize &Chatterbots/Chatbots (1) In 1990, Hugh Loebner started an annual Turing Test competition. Conversational Programs are nowadays called Chatterbots (or Chatbots). $100,000 to the first bot that judges cannot distinguish from a real human in a Turing test that includes text, visual, and auditory input. The Prize dissolves once the $100,000 prize is won.

12. Loebner Prize &Chatterbots/Chatbots (2) Nobody has won the prize yet.

13. Real-World Applications of NLP NLP can be stand-along applications or components embedded in other systems. Major NLP applications include: Machine translation Question answering Summarization Conversational agents

14. Commercial World • Lot’s of exciting stuff going on… Powerset Source: Jurafsky & Martin “Speech and Language Processing”

15. 1. Machine Translation (MT) One of the very earliest pursuits in computer science (after WWII). Basic approaches: Inter-lingual (rule-based) Direct translation (corpus-based)  morepopular these days Example: Google Translate MT engine (based on SYSTRAN system developed in EC).

16. 2. Question Answering Finds an answer (not a document) to a question typed in as a natural language sentence (not keywords). Most systems can only answer simple, trivial pursuit type questions. Example: Ask.com Some search engines perform limited, phrase-based Q&A, e.g. Google

17. 3. Text Summarization Create a summary of a text or texts. Many difficult problems, including: Paraphrases Anaphora (e.g.“it”, “they”)

18. 4. Analyzing Web Documents Recently there have been many NLP applications which analyze (not just retrieve) weblogs, discussion forums, message boards, user groups, and other forms of user generated media Product marketing information Political opinion tracking Social network analysis Buzz analysis (what’s hot, what topics are people talking about right now). Source: Jurafsky & Martin “Speech and Language Processing”

19. 5. NLP in IR • Query expansion • Add synonyms, related words to the query terms to improve search results. • Example: Google AdWords tool • NOTE: Stemming is NOT a NLP technique!!!

20. Source: Marti Hearst, i256, at UC Berkeley

21. NLP Tasks • Those NLP applications require several NLP analyses: • Word tokenization • Sentence boundary detection • Part-of-speech (POS) tagging • to identify the part-of-speech (e.g. noun, verb) of each word • Named Entity (NE) recognition • to identify proper nouns (e.g. names of person, location, organization; domain terminologies) • Parsing • to identify the syntactic structure of a sentence • Semantic analysis • to derive the meaning of a sentence

22. Different Levels of Language Analysis • Phonology • Speech audio signal to phonemes • Morphology • Inflection (e.g. “I”, “my”, “me”; “eat”, “eats”, “ate”, “eaten”) • Derivation (e.g. “teach”, “teacher”, “nominate”, “nominee”) • Syntax • Part-of-speech (noun, verb, adjective, preposition, etc.) • Phrase structure (e.g. noun phrase, verb phrase) • Semantics • Meaning of a word (e.g. “book” as a bound volume or an accounting ledger) or a sentence • Discourse • Meaning and inter-relation between sentences

23. Why is NLP so hard..? Understanding natural languages is hard … because of inherent ambiguity Engineering NLP systems is also hard … because of: Huge amount of data resources needed (e.g. grammar, dictionary, documents to extract statistics from) Computational complexity (intractable) of analyzing a sentence

24. Ambiguity (1) “Get the cat with the gloves.” Source: Marti Hearst, i256, at UC Berkeley

25. Ambiguity (2) Find at least 5 meanings of this sentence: “I made her duck” • I cooked waterfowl for her benefit (to eat) • I cooked waterfowl belonging to her • I created the (plaster?) duck she owns • I caused her to quickly lower her head or body • I waved my magic wand and turned her into undifferentiated waterfowl Source: Jurafsky & Martin “Speech and Language Processing”

26. Ambiguity (3) Some ambiguous headlines Juvenile Court to Try Shooting Defendant Teacher Strikes Idle Kids Kids Make Nutritious Snacks Bush Wins on Budget, but More Lies Ahead Hospitals are Sued by 7 Foot Doctors Source: Marti Hearst, i256, at UC Berkeley

27. Ambiguity is Pervasive • Phonetics • I mate or duck • I’m eight or duck • Eye maid; her duck • Aye mate, her duck • I maid her duck • I’m aid her duck • I mate her duck • I’m ate her duck • I’m ate or duck • I mate or duck Sound like“I made her duck” Source: Jurafsky & Martin “Speech and Language Processing”

28. “I saw a man on the hill with a telescope” “I saw a man on the hill with a hat” • Lexical category (part-of-speech) • “duck” as a noun or a verb • Lexical Semantics (word meaning) • “duck” as an animal or a plaster duck statue • Compound nouns • e.g. “dog food”, “Intelligent design scores …” • Syntactic ambiguity • [But semantics can sometimes help disambiguate]

29. Dealing with Ambiguity Four possible approaches: • Formal approaches -- Tightly coupled interaction among processing levels; knowledge from other levels can help decide among choices at ambiguous levels. • Pipeline processing that ignores ambiguity as it occurs and hopes that other levels can eliminate incorrect structures. • Probabilistic approaches based on making the most likely choices • Don’t do anything, maybe it won’t matter Source: Jurafsky & Martin “Speech and Language Processing”

30. The Bottom Line Complete NL Understanding (thus general intelligence) is impossible. But we can make incremental progress. Also we have made successes in limited domains. [But NLP is costly – Lots of work and resources are needed, but the amount of return is sometimes not worth it.]

31. Statistical Approaches to NLP • Get large text collections (corpora) • Compute statistics over the words in those collections • Surprising results: • Getting more data is better than fine-tuning algorithms! Banko & Brill ‘01 Source: Marti Hearst, i256, at UC Berkeley

32. Sentence Analysis S NP V NP “John” “ate” “the cake” “John ate the cake” Syntactic structure Semantic structure (ACTION ingest (ACTOR John-1) (PATIENT food))

33. Syntactic Parsing S Grammar R0: ® S NP VP NP VP R1: ® NP Det N R2: ® VP VG NP R3: ® VG V NP V R4: ® NP " John" R5: ® V " ate" Det N R6: “John” “ate” ® Det " the" R7: ® N " cake" “the” “cake” • The process of deriving the phrase structure of a sentence is called “parsing”. • The structure (often represented by a Context-Free parse tree) is based on the grammar.

34. Parsing Algorithms Top-down Parsing -- (top-down) derivation Bottom-up Parsing Chart Parsing Earley’s Algorithm – most efficient, O(n3) Left-corner Parsing – optimization of Earley’s and lots more…

35. (Bottom-up) Chart Parsing “John ate the cake” 0 1 2 3 4 Grammar (11) reduce (10) reduce --- (5) shift 2 (9) reduce --- (2) shift 2 (4) shift 2 (7) shift 2 --- (1) shift 1 “John” (3) shift 1 “ate” (6) shift 1 “the” (8) shift 1 “cake” 0 1 2 3 4

36. Earley’s Algorithm “John ate the cake” 0 1 2 3 4 Grammar (12) completor (1) predictor (11) completor (2) scanner “John” (3) predictor (4) predictor (10) completor (6) completor (7) predictor (5) scanner “ate” (9) scanner “cake” (8) scanner “the”

37. Demo using my CF parser

38. Probabilistic Parsing For ambiguous sentences, we’d like to know which parse tree is more likely than others. So we must assign probability to each parse tree … but how? A probability of a parse tree t is where r is a rule used in t. and p(r) is obtained from a (annotated) corpus.

39. Partial Parsing Parsing fails when the coverage of the grammar is not complete – but it’s almost impossible to write out all legal syntax (without accepting ungrammatical sentences). We’d like to at least get pieces even when full parsing fails. Why not abandon full parsing and aim for partial parsing from the start…

40. Semantic Analysis (1) • Derive the meaning of a sentence. • Often applied on the result of syntactic analysis. • “Johnatethe cake.” • NP V NP • ((action INGEST) ; syntactic verb (actor JOHN-01) ; syntactic subj (object FOOD)) ; syntactic obj • To do semantic analysis, we need a (semantic) dictionary (e.g. WordNet, http://www.cogsci.princeton.edu/~wn/).

41. Semantic Analysis (2) • Semantics is a double-edged sword… • Can resolve syntactic ambiguity • “I saw a man on the hill with a telescope” • “I saw a man on the hill with a hat” • But introduces semantic ambiguity • “She walked towards the bank” • But in human sentence processing, we seem to resolve both types of ambiguities simultaneously (and in linear time as well)…

42. Demo using my Unification parser