1. CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 38–Universal Networking Language) Pushpak Bhattacharyya CSE Dept., IIT Bombay 14 th April, 2011

2. A Perpective Morphology Lexicon Syntax Semantics Pragmatics Discourse

3. UNL: a United Nations project Started in 1996 10 year program 15 research groups across continents First goal: generators Next goal: analysers (needs solving various ambiguity problems) Current active language groups UNL_French (GETA-CLIPS, IMAG) UNL_English+Hindi UNL_Italian (Univ. of Pisa) UNL_Portugese (Univ of Sao Paolo, Brazil) UNL_Russian (Institute of Linguistics, Moscow) UNL_Spanish (UPM, Madrid) 3

4. 4 World-wide Universal Networking Language (UNL) Project UNL English Russian Japanese Hindi Spanish Language independent meaning representation. Marathi Others

5. Foundations and Applications UNL Foundations Semantic Relations Universal Words Attributes How to write UNL expressions UNL Applications Machine Translation: Rule based and Statistical Search Text Entailment Sentiment Analysis 5

6. UNL represents knowledge: John eats rice with a spoon Semantic relations attributes Universal words Repository of 42 Semantic Relations and 84 attribute labels 6

7. Sentence embeddings Deepa claimed that she had composed a poem. [UNL] agt(claim.@entry.@past, Deepa) obj(claim.@entry.@past, :01) agt:01(compose.@past.@entry.@complete, she) obj:01(compose.@past.@entry.@complete, poem.@indef) [\UNL] 7

8. English sentences: basic structure A B John eats bread agt(eat.@entry, John) obj(eat.@entry, bread) A John sleeps aoj(sleep.@entry, John) A B John is good aoj(good.@entry, John) verb A R1R1 R2R2 B A aoj verb BA R1R1 R2R2

9. Hindi sentences: basic structure A B John roti khaataa hai agt(eat.@entry, John) obj(eat.@entry, bread) A John sotaa hai aoj(sleep.@entry, John) A B John acchaa hai aoj(good.@entry, John) verb A R1R1 R2R2 B A aoj verb BA R1R1 R2R2

10. :02 :01 Complex English sentences: Use recursion on the basic structure A B John who is a good boy eats bread which is toasted agt(eat.@entry, :01) obj(eat.@entry, :02) aoj:01(boy, John.@entry) mod:01(boy, good) obj:01(toast, bread.@entry.@focus) boy John aoj toast Bread obj eat :02:01 agtobj good mod Red arrows indicate entry nodes

11. 11 Constituents of Universal Networking Language Universal Words (UWs) Relations Attributes Knowledge Base

12. 12 What is a Universal Word (UW)? Words of UNL Constitute the UNL vocabulary, the syntactic- semantic units to form UNL expressions A UW represents a concept Basic UW (an English word/compound word/phrase with no restrictions or Constraint List) Restricted UW (with a Constraint List ) Examples: “crane(icl>device)” “crane(icl>bird)”

13. 13 The Lexicon Format of the dictionary entry e.g., [minister] {} “minister(icl>person)” (N,ANIMT,PHSCL,PRSN); Head word Universal word Attributes Morphological- Pl(plural), V_ed(past tense form) Syntactic - V(verb),VOA(verb of action) Semantic - ANIMT(animate), PLACE, TIME [headword] {} “Universal word“ (Attribute list);

14. 14 The Lexicon (cntd) Content words: [forward] {} “forward(icl>send)” (V,VOA) ; [mail] {} “mail(icl>message)” (N,PHSCL,INANI) ; [minister] {} “minister(icl>person)” (N,ANIMT,PHSCL,PRSN) ; HeadwordUniversal WordAttributes He forwarded the mail to the minister.

15. 15 The Lexicon (cntd) function words: [he] {} “he” (PRON,SUB,SING,3RD) [the] {} “the” (ART,THE) ; [to] {} “to” (PRE,#TO) ; HeadwordUniversal Word Attributes He forwarded the mail to the minister.

16. Multilingual dictionary सार्वभौम शब्द मुख्य शब्द farmer(icl>creator)farmer शेतकरी किसान N,M,ANIMT,FAUNA,MML,PRSN,Na N,ANIMT,FAUNA,MML,PRSN E M H N,M,ANIMT,FAUNA,MML,PRSN गुण

17. 17 The Features of a UW Every concept existing in any language must correspond to a UW The constraint list should be as small as necessary to disambiguate the headword Every UW should be defined in the UNL Knowledge-Base (now wordnet)

18. 18 Restricted UWs Examples He will hold office until the spring of next year. The spring was broken. Restricted UWs, which are Headwords with a constraint list, for example: “spring(icl>season)” “spring(icl>device)” “spring(icl>jump)” “spring(icl>fountain)”

19. 19 How to create UWs? Pick up a concept the concept of “crane" as "a device for lifting heavy loads” or as “a long-legged bird that wade in water in search of food” Choose an English word for the concept. In the case for “crane", since it is a word of English, the corresponding word should be ‘crane' Choose a constraint list for the word. [ ] ‘crane(icl>device)' [ ] ‘crane(icl>bird)'

20. Example: Hindi word ghar ghar- house usne garmii me ghar kii marammat kii he renovated the house in the summer ghar- home office ke baad ghar louto return home after office Ghar- family bade ghar kii betii girl from a renowned family 20

21. Example: ghar (cntd) ghar- own country bahut saal bidesh me kaam karke ghar louta aayaa returned home after working abroad for many years Ghar- astrological position ashtam ghar par budh hai Mercury in in the eighth house 21

22. House in English Wordnet 1. (1029) house -- (a dwelling that serves as living quarters for one or more families; "he has a house on Cape Cod"; "she felt she had to get out of the house") 3. (51) house -- (a building in which something is sheltered or located; "they had a large carriage house") 4. (39) family, household, house, home, menage -- (a social unit living together; "he moved his family to Virginia"; "It was a good Christian household“;) 22

23. House in English Wordnet 7. (13) house -- (aristocratic family line; "the House of York") 11. sign of the zodiac, star sign, sign, mansion, house, planetary house -- ((astrology) one of 12 equal areas into which the zodiac is divided) 23

24. Unambiguous construction of UWs Use constraints: Ontological, Semantic and Argument Example: forward a mail to the minister forward(icl>do, icl>send, agt>thing(icl>animate), obj>thing(icl>inanimate), gol>thing) Constraint types: icl>do: ontological, icl>send: semantic agt>thing, obj>thing, gol>thing: argument

25. UNL Relations

26. Relations constitute the syntax of UNL Express how concepts (UWs) constitute a sentence Represented as strings of 3 characters or less A set of 41 relations specified in UNL (e.g., agt, aoj, ben, gol, obj, plc, src, tim,…) Refer to a semantic role between two lexical items in a sentence

27. 27 AGT / AOJ / OBJ AGT (Agent) Definition: Agt defines a thing which initiates an action AOJ (Thing with attribute) Definition: Aoj defines a thing which is in a state or has an attribute OBJ (Affected thing) Definition: Obj defines a thing in focus which is directly affected by an event or state

28. 28 Examples John broke the window. agt ( break.@entry.@past, John) This flower is beautiful. aoj ( beautiful.@entry, flower) He blamed John for the accident. obj ( blame.@entry.@past, John)

29. Example: UNL Graph with agt, obj, ben obj agt @ entry @ past baby(icl>child) carve(icl>cut) toy(icl>plaything) he(iof>person) @def ben He carved a toy for the baby.

30. 30 GOL / SRC GOL (Goal : final state) Definition: Gol defines the final state of an object or the thing finally associated with an object of an event SRC (Source : initial state) Definition: Src defines the initial state of object or the thing initially associated with object of an event

31. 31 GOL I deposited my money in my bank account. objagt @ entry @ past account(icl>statement) deposit(icl>put) money(icl>currency) I gol bank(icl>possession) mod I I

32. 32 SRC They make a small income from fishing. obj agt @ entry @ present fishing(icl>business) make(icl>do) income(icl>gain) they(icl>persons) src small(aoj>thing) mod

33. 33 PUR PUR (Purpose or objective) Definition: Pur defines the purpose or objectives of the agent of an event or the purpose of a thing exist This budget is for food. pur ( food.@entry, budget ) mod ( budget, this )

34. 34 RSN RSN(Reason) Definition: Rsn defines a reason why an event or a state happens They selected him for his honesty. agt(select(icl>choose).@entry, they) obj(select(icl>choose).@entry, he) rsn (select(icl>choose).@entry, honesty)

35. 35 TIM TIM (Time) Definition: Tim defines the time an event occurs or a state is true I wake up at noon. agt ( wake up.@entry, I ) tim ( wake up.@entry, noon(icl>time))

36. 36 PLC PLC (Place) Definition: Plc defines the place an event occurs or a state is true or a thing exists Temples are very famous in India. aoj (famous.@entry, temple@pl ) man (famous.@entry, very) plc (famous.@entry, India)

37. 37 INS INS (Instrument) Definition: Ins defines the instrument to carry out an event I solved it with computer agt ( solve.@entry.@past, I ) ins ( solve.@entry.@past, computer ) obj ( solve.@entry.@past, it )

38. 38 INS obj agt @ entry @ past blanket(icl>object) cover(icl>do) baby(icl>child) John(iof>person) @def ins John covered the baby with a blanket.

39. 39 Attributes Constitute syntax of UNL Play the role of bridging the conceptual world and the real world in the UNL expressions Show how and when the speaker views what is said and with what intention, feeling, and so on Seven types: Time with respect to the speaker Aspects Speaker’s view of reference Speaker’s emphasis, focus, topic, etc. Convention Speaker’s attitudes Speaker’s feelings and viewpoints

40. 40 Tense: @past The past tense is normally expressed by @past {unl} agt(go.@entry.@past, he) … {/unl} He went there yesterday

41. 41 Aspects: @progress {unl} man ( rain.@entry.@present.@progress, hard ) {/unl} It’s raining hard.

42. 42 Speaker’s view of reference @def (Specific concept (already referred)) The house on the corner is for sale. @indef (Non-specific class) There is a book on the desk @not is always attached to the UW which is negated. He didn’t come. agt ( come.@entry.@past.@not, he )

43. 43 Speaker’s emphasis @emphasis John his name is. mod ( name, he ) aoj ( John.@emphasis.@entry, name ) @entry d enotes the entry point or main UW of an UNL expression

44. How to generate UNL

45. 45 Early Enco (1996-98) Analysis windows - Two in number Left Analysis Window (LAW) Right Analysis Window (RAW) Condition windows - Many in number Left Condition Window (LCW) Right Condition Window (RAW) LAW Word 2 Word 1 Word 4 … RAW RCW Word n LCW Word 3 sentence windows

46. 46 UNL Rule for a Semantic Relation ; Create relation between V and N2, after resolving the preposition preceding N2 < {V,VOA,:::}{N,TIME,DAY,ONRES,PRERES::tim:}P25; IF the left analysis window is on a verb(V) which is verb of action (VOA) AND the right analysis window is on a noun (N) and has TIME, DAY attribute for which the preceding preposition (on) has been processed and deleted THEN set up the tim relation between V and N 2. (indicated by < at the start of the rule)

47. UNL generation using NLP tools and resources 47

48. SRS based system 48

49. Multi parser based system

50. Evaluation Recall = #expressions matched in gold and generated UNL #expressions expected in gold UNL Precision = #expressions matched in gold and generated UNL #expressions in generated UNL F1 score = 2 * recall * precision recall + precision

51. Comparison between the two systems Table NameAccuracy of XLE Parser Based System Accuracy of Multi-parser based system evalTb_OXF_V_TO_INF0.83760.8591 evalTb_OXF_VN_TO_INF0.83690.8429 evalTb_OXF_S_TO_DO_VERB0.7833 evalTb_XTAG0.71810.7835 evalTb_FRAMENET0.66180.7591 evalTb_RADFORD0.81410.8542 evalTb_V0.59200.7587 evalTb_VN0.75280.7625 evalTb_VNN0.76920.7902 evalTb_VING0.7084 evalTb_VADJ0.54860.6214 evalTb_VINF0.72360.7772 evalTb_VTHAT0.79880.7999 evalTb_TOI_Education0.38750.3669 evalTb_test0.4667 evalTb_demo1.0000 evalTb_Test20.39130.5116 evalTb_t30.71550.8553 evalTb_Barcelona0.31940.3181 Total 0.6489 0.7010

52. Language Processing & Understanding Information Extraction: Part of Speech tagging Named Entity Recognition Shallow Parsing Summarization Machine Learning: Semantic Role labeling Sentiment Analysis Text Entailment (web 2.0 applications) Using graphical models, support vector machines, neural networks IR: Cross Lingual Search Crawling Indexing Multilingual Relevance Feedback Machine Translation: Statistical Interlingua Based English  Indian languages Indian languages  Indian languages Indowordnet Resources: http://www.cfilt.iitb.ac.inhttp://www.cfilt.iitb.ac.in Publications: http://www.cse.iitb.ac.in/~pbhttp://www.cse.iitb.ac.in/~pb Linguistics is the eye and computation the body Use of UNL in multiple NLP tasks

53. Summing up Some NLP milestones covered WSD: various approaches SMT Parsing (classical and probabilistic) Phonology, Phonetics, Syllabification, Transliteration Semantics, UNL Assignments: to reinforce understanding of lectures Important topics left out: IR, Similarity measures Seminars: wide range of topics for breadth and exposure Lectures: Foundation and depth

54. God Bless!!