Natural Language Processing

1. Natural Language Processing

2. Why “natural language”? • Natural vs. artificial • Language vs. English

3. Why “natural language”? • Natural vs. artificial • Not precise, ambiguous, wide range of expression • Language vs. English • English, French, Japanese, Spanish

4. Why “natural language”? • Natural vs. artificial • Not precise, ambiguous, wide range of expression • Language vs. English • English, French, Japanese, Spanish • Natural language processing = programs, theories towards understanding a problem or question in natural language and answering it

5. Approaches • System building • Interactive • Understanding only • Generation only • Theoretical • Draws on linguistics, psychology, philosophy

6. Building an NL system is hard • Unlikely to be possible without solid theoretical underpinnings

7. Natural language is useful • Question-answering systems • http://tangra.si.umich.edu/clair/NSIR/NSIR.cgi • Mixed initiative systems • http://www.cs.columbia.edu/~noemie/match.mpg • Information extraction • http://nlp.cs.nyu.edu/info-extr/biomedical-snapshot.jpg • Systems that write/speak • http://www-2.cs.cmu.edu/~awb/synthesizers.html • MAGIC • Machine translation • http://world.altavista.com/babelfish

8. Topics • Syntax • Semantics • Pragmatics • Statistical NLP: combining learning and NL processing

9. Goal of Interpretation • Identify sentence meaning • Do something with meaning • Need some representation of action/meaning

10. Analysis of form: Syntax • Which parts were damaged by larger machines? • Which parts damaged larger machines? • Which larger machines damaged parts? • Approaches: • Statistical part of speech tagging • Parsing using a grammar • Shallow parsing: identify meaningful chunks

11. Which parts were damaged by larger machines? S (Q) VP NP N NP (Q) ADJ V (past) machines damage Det (Q) N larger parts which

12. Which parts were damaged by machines? – with functional roles S (Q) VP NP (SUBJ) ADJ N NP (Q) (OBJ) V (past) damage Det (Q) N larger machines parts which

13. Which parts damaged machines? – with functional roles NP (Q) (SUBJ) Det (Q) N which S (Q) VP NP (OBJ) V (past) N ADJ parts damage larger machines

14. Parsers • Grammar • S -> NP VP • NP -> DET {ADJ*} N • Different types of grammars • Context Free vs. Context Sensitive • Lexical Functional Grammar vs. Tree Adjoining Grammars • Different ways of acquiring grammars • Hand-encoded vs. machine learned • Domain independent (TreeBank, Wall Street Journal) • Domain dependent (Medical texts)

15. Semantics: analysis of meaning • Word meaning • John picked up a bad cold • John picked up a large rock. • John picked up Radio Netherlands on his radio. • John picked up a hitchhiker on Highway 66. • Phrasal meaning • Baby bonuses -> allocations • Senior citizens -> personnes agees • Causing havoc -> seme le dessaroi • Approaches • Representing meaning • Statistical word disambiguation • Symbolic rule-based vs. shallow statistical semantics

16. Representing Meaning - WordNet

18. OMEGA • http://omega.isi.edu:8007/index • http://omega.is.edu/doc/browsers.html

20. Statistical Word Sense Disambiguation Context within the sentence determines which sense is correct • The candidate picked up [sense6] thousands of additional votes. • He picked up [sense2] the book and started to read. • Her performance in school picked up [sense13]. • The swimmers got out of the river and climbed the bank [sloping land] to retrieve their towels. • The investors took their money out of the bank [financial institution] and moved it into stocks and bonds.

21. Goal • A program which can predict which sense is the correct sense given a new sentence containing “pick up” or “bank” • Avoid manually itemizing all words which can occur in sentences with different meanings • Can we use machine learning?

22. What do we need? • Data • Features • Machine Learning algorithm • Decision tree vs. SVM/Naïve Bayes • Inspecting the output • Accuracy of these methods

23. Using Categories from Roget’s Thesaurus (e.g., machine vs. animal) for training

24. Training data for “machines”

26. Predicting the correct sense in unseen text • Use presence of the salient words in context • 50 word window • Use Baye’s rule to compute probabilities for different categories

27. “Crane” • Occurred 74 times in Grolliers, 36 as animal, 38 as machine • Prediction in new sentences were 99% correct • Example: lift water and to grind grain .PP Treadmills attached to cranes were used to lift heavy objects from Roman times.

30. Going Home – A play in one act • Scene 1: Pennsylvania Station, NYCBonnie: Long Beach?Passerby: Downstairs, LIRR Station • Scene 2: ticket counter: LIRRBonnie: Long Beach?Clerk: $4.50 • Scene 3: Information Booth, LIRRBonnie: Long Beach?Clerk: 4:19, Track 17 • Scene 4: On the train, vicinity of Forest HillsBonnie: Long Beach?Conductor: Change at Jamaica • Scene 5: On the next train, vicinity of LynbrookBonnie: Long Beach?Conductor: Rigtht after Island Park.

31. Question Answering on the web • Input: English question • Data: documents retrieved by a search engine from the web • Output: The phrase(s) within the documents that answer the question

32. Examples • When was X born? • When was Mozart born? • Mozart was born in 1756. • When was Gandhi born? • Gandhi (1869-1948) • Where are the Rocky Mountains located? • What is nepotism?

33. Common Approach • Create a query from the question • When was Mozart born -> Mozart born • Use WordNet to expand terms and increase recall: • Which high school was ranked highest in the US in 1998? • “high school” -> (high&school)|(senior&high&school)|(senior&high(|high|highschool • Use search engine to find relevant documents • Pinpoint passage within document that has answer using patterns • From IR to NLP

34. PRODUCE A BIOGRAPHY OF [PERON].Only these fields are Relevant: • Name(s), aliases: • *Date of Birth or Current Age: • *Date of Death: • *Place of Birth: • *Place of Death: • Cause of Death: • Religion (Affiliations): • Known locations and dates: • Last known address: • Previous domiciles: • Ethnic or tribal affiliations: • Immediate family members • Native Language spoken: • Secondary Languages spoken: • Physical Characteristics • Passport number and country of issue: • Professional positions: • Education • Party or other organization affiliations: • Publications (titles and dates):

35. Biography of Han Ming • Han Ming, born 1944 March in Pyongyan, South Korean Lei Fa Women’s University in French law, literature, a former female South Korean people, chairman of South Korea women’s groups,…Han, 62, has championed women’s rights and liberal political ideas. Han was imprisoned from 1979 to 1981 on charges of teaching pro-Communist ideas to workers, farmers and low-income women. She became the first minister of gender equality in 2001 and later served as an environment minister.

36. Biography – two approaches • To obtain high precision, we handle each slot independently using bootstrapping to learn IE patterns. • To improve the recall, we utilize a biography Language Model.

37. Approach • Characteristics of the IE approach • Training resource: Wikipedia and its manual annotations • Bootstrapping interleaves two corpora to improve precision • Wikipedia: reliable but small • Web: noisy but many relevant documents • No manual annotation or automatic tagging of corpus • Use seed tuples (person, date-of-birth) to find patterns • This approach is scalable for any corpus • Irrespective of size • Irrespective of whether it is static or dynamic • The IE system is augmented with language models to increase recall

38. Biography as an IE task • We need patterns to extract information from a sentence • Creating patterns manually is a time consuming task, and not scalable • We want to find these patterns automatically

39. Biography patterns from Wikipedia

40. Biography patterns from Wikipedia • Martin Luther King, Jr., (January 15, 1929 – April 4, 1968) was the most … • Martin Luther King, Jr., was born on January 15, 1929, in Atlanta, Georgia.

41. Run IdFinder on these sentences • <Person> Martin Luther King, Jr. </Person>, (<Date>January 15, 1929</Date> – <Date> April 4, 1968</Date>) was the most… • <Person> Martin Luther King, Jr. </Person>, was born on <Date> January 15, 1929 </Date>, in <GPE> Atlanta, Georgia </GPE>. • Take the token sequence that includes the tags of interest + some context (2 tokens before and 2 tokens after)

42. Convert to Patterns: • <My_Person>(<My_Date>– <Date>) was the • <My_Person> , was born on <My_Date>, in • Remove more specific patterns – if there is a pattern that contains other, take the smallest > k tokens. • <MY_Person> , was born on <My_Date> • <My_Person>(<My_Date>– <Date>) • Finally, verify the patterns manually to remove irrelevant patterns.

43. Examples of Patterns: • 502 distinct place-of-birth patterns: • 600 <MY_Person> was born in <MY_GPE> • 169 <MY_Person> ( born <Date> in <MY_GPE> ) • 44 Born in <MY_GPE> <MY_Person> • 10 <MY_Person> was a native <MY_GPE> • 10 <MY_Person> 's hometown of <MY_GPE> • 1 <MY_Person> was baptized in <MY_GPE> • … • 291 distinct date-of-death patterns: • 770 <MY_Person> ( <Date> - <MY_Date> ) • 92 <MY_Person> died on <MY_Date> • 19 <MY_Person> <Date> - <MY_Date> • 16 <MY_Person> died in <GPE> on <MY_Date> • 3 < MY_Person> passed away on < MY_Date > • 1 < MY_Person> committed suicide on <MY_Date> • …

44. Biography as an IE task • This approach is good for the consistently annotated fields in Wikipedia: place of birth, date of birth, place of death, date of death • Not all fields of interests are annotated, a different approach is needed to cover the rest of the slots

45. Bouncing between Wikipedia and Google • Use one seed only: • <my person> and <target field> • Google: “Arafat” “civil engineering”, we get:

47. Bouncing between Wikipedia and Google • Use one seed only: • <my person> and <target field> • Google: “Arafat” “civil engineering”, we get: • Arafatgraduated with a bachelor’s degree in civil engineering • Arafatstudiedcivil engineering • Arafat,acivil engineering student • … • Using these snippets, corresponding patterns are created, then filtered out manually.

48. Bouncing between Wikipedia and Google • Use one seed tuple only: • <my person> and <target field> • Google: “Arafat” “civil engineering”, we get: • Arafatgraduated with a bachelor’s degree incivil engineering • Arafatstudiedcivil engineering • Arafat,acivil engineering student • … • Using these snippets, corresponding patterns are created, then filtered out manually • To get more seed pairs, go to Wikipedia biography pages only and search for: • “graduated with a bachelor’s degree in” • We get:

50. Bouncing between Wikipedia and Google • New seed tuples: • “Burnie Thompson” “political science“ • “Henrey Luke” “Environment Studies” • “Erin Crocker” “industrial and management engineering” • “Denise Bode” “political science” • … • Go back to Google and repeat the process to get more seed patterns!