1. NLP

2. Introduction to NLP

3. (U)nderstanding and (G)eneration Language Computer (U) Language (G)

4. Natural language generation is the process of deliberately constructing a natural language text in order to meet specified communicative goals. [McDonald 1992]

5. Mapping meaning to text Stages: –Content selection –Lexical choice –Sentence structure: aggregation, referring expressions –Discourse structure

6. FOG (Goldberg et al. 1994) Weather forecast reports for the Canadian Weather Service Input –Numerical simulation data annotated by humans

7. Function: –Produces a report describing the simulation options that an engineer has explored Input –A simulation log file Developer –Bellcore and Columbia University

8. RUNID fiberall FIBER 6/19/93 act yes FA 1301 2 1995 FA 1201 2 1995 FA 1401 2 1995 FA 1501 2 1995 ANF co 1103 2 1995 48 ANF 1201 1301 2 1995 24 ANF 1401 1501 2 1995 24 END. 856.0 670.2

9. This saved fiber refinement includes all DLC changes in Run-ID ALLDLC. RUN-ID FIBERALL demanded that PLAN activate fiber for CSAs 1201, 1301, 1401 and 1501 in 1995 Q2. It requested the placement of a 48-fiber cable from the CO to section 1103 and the placement of 24-fiber cables from section 1201 to section 1301 and from section 1401 to section 1501 in the second quarter of 1995. For this refinement, the resulting 20 year route PWE was $856.00K, a $64.11K savings over the BASE plan and the resulting 5 year IFC was $670.20K, a $60.55K savings over the BASE plan.

10. NLG is about choices –Content –Coherence –Style –Media –Syntax –Aggregation –Referring expressions –Lexical choice

11. Introduction to NLP

12. Example –The dogs bites (agreement) Example –many water (count/mass nouns) Idea –S  NP VP (if the person of the NP is equal to the person of the VP)

13. Types of unification grammars –LFG, HPSG, FUG Handle agreement –e.g., number, gender, person Unification –Two constituents can be combined only if their features can unify

14. CAT NP PERSON 3 NUMBER SINGULAR CAT NP NUMBER SINGULAR PERSON 3 CAT NP NUMBER SINGULAR PERSON 3 U

15. CAT NP PERSON 3 NUMBER SINGULAR CAT NP PERSON 1 PERSON 3 U FAILURE

16. S  NP VP {NP PERSON} = {VP PERSON} S  Aux NP VP {Aux PERSON} = {NP PERSON} Verb  bites {Verb PERSON} = 3 Verb  bite {Verb PERSON} = 1

17. VP  Verb {VP SUBCAT} = {Verb SUBCAT} {VP SUBCAT} = INTRANS VP  Verb NP {VP SUBCAT} = {Verb SUBCAT} {VP SUBCAT} = TRANS VP  Verb NP NP {VP SUBCAT} = {Verb SUBCAT} {VP SUBCAT} = DITRANS

18. Language is viewed as a resource for expressing meaning in context (Halliday, 1985) Layers: mood, transitivity, theme The systemwillsavethe document Moodsubjectfinitepredicatorobject Transitivityactorprocessgoal Themethemerheme

19. ( :process save-1 :actor system-1 :goal document-1 :speechact assertion :tense future )  Input is underspecified

20. Based on Kay’s (83) formalism Partial information, declarative, uniform, compact Same framework used for all stages: syntactic realization, lexicalization, and text planning

21. Functional vs. structured analysis “John eats an apple” Actor (John), affected (apple), process (eat) Suitable for generation

22. Voice: An apple is eaten by John Tense: John ate an apple Mode: Did John eat an apple? Modality: John must eat an apple action = eat actor = John object = apple

23. Target sentence Input FD Grammar Unification process Linearization process

24. View an FD as a tree To specify features, use a path –{feature feature … feature} value –e.g. {prot number} Also use relative paths –{^ number} value = the feature number for the current node –{^ ^ number} value = the feature number for the node above the current node

25. ((cat s) (prot ((n ((lex john))))) (verb ((v ((lex like))))) (goal ((n ((lex mary))))))

26. ((alt top (((cat s) (prot ((cat np))) (goal ((cat np))) (verb ((cat vp) (number {prot number}))) (pattern (prot verb goal))) ((cat np) (n ((cat noun) (number {^ ^ number}))) (alt (((proper yes) (pattern (n))) ((proper no) (pattern (det n)) (det ((cat article) (lex “the”))))))) ((cat vp) (pattern (v)) (v ((cat verb)))) ((cat noun)) ((cat verb)) ((cat article)))))

27. ((cat s) (goal ((cat np) (n ((cat noun) (lex mary) (number {goal number}))) (pattern (n)) (proper yes))) (pattern (prot verb goal)) (prot ((cat np) (n ((cat noun) (lex john) (number {verb number}))) (number {verb number}) (pattern (n)) (proper yes))) (verb ((cat vp) (pattern (v)) (v ((cat verb) (lex like))))))

34. Syntactic realization front-end Variable level of abstraction 5,600 branches and 1,600 alts Lexical chooser SURGE Linearizer Morphology Lexicalized FDSyntactic FD Text

35. NLP