CS5545: Realisation

1. CS5545: Realisation Background Reading: Building Natural Language Generation Systems, chap 6

2. Realisation • Third (last) NLG stage • Generate actual text • Take care of details of language • Syntactic details • Eg Agreement (the dog runs vs the dogs run) • Morphological details • Eg, plurals (dog/dogs vs box/boxes) • Presentation details • Eg, fit to 80 column width

3. Realisation • Problem: There are lots of finicky details of language which most people developing NLG systems don’t want to worry about • Solution: Automate this using a realiser

4. Syntax • Sentences must obey the rules of English grammar • Specifies which order words should appear in, extra function words, word forms • Many aspects of grammar are somewhat bizarre

5. Syntactic Details: Verb Group • Verb group is the main verb plus helping words (auxiliaries). • Encodes information in fairly bizarre ways, eg tense • John will watch TV (future – add will) • John watches TV (present - +s form of verb for third-person singular subjects) • John is watching TV (progressive – form of BE verb, plus +ing form of verb) • John watched TV (past – use +ed form of verb)

6. Verb group • Negation • Usually add “not” after first word of verb group • John will not watch TV • Exception: add “do not” before 1-word VG • Inflections on do, use infinitive form of main verb • John did not watch TV • Exception to exception: use first strategy if verb is form of BE • John is not happy

7. Realiser • Just tell realiser verb, tense, whether negated, and it will figure out the VG • (watch, future) -> will watch • (watch, past, negated) -> did not watch • Etc • Similarly automate other “obscure” encodings of information

8. Morphology • Words have different forms • Nouns have plural • Dog, dogs • Verbs have 3ps form, participles • Watch, watches, watching, watched • Adjectives have comparative, superlative • Big, bigger, biggest

9. Formation of variants • Example: plural • Usually add “s” (dogs) • But add “es” if base noun ends in certain letters (boxes, guesses) • Also change final “y” to “i” (tries) • Many special cases • children (vs childs), people (vs persons), etc

10. Realiser • Calculates variants automatically • (dog, plural) -> dogs • (box, plural) -> boxes • (child, plural) -> children • etc

11. Punctuation • Rules for structures • Sentences have first word capitalised, end in a full stop • My dog ate the bone, the meat, etc.. • Lists have conjunction (eg, and) between last two elements, comma between others • I saw Tom, Sue, Zoe, and Ciaran at the meeting. • Etc • Realiser can automatically insert appropriate punctuation for a structure

12. Punctuation • Rules on combinations of punc • Don’t end full stop if sentence already ends in a full stop • He lives in Washington D.C. • He lives in Washington D.C.. • Brackets absorb some full stops • John lives in Aberdeen (he used to live in Edinburgh). • John lives in Aberdeen (he used to live in Edinburgh.). • Again realiser can automate

13. Pouring • Usually we insert spaces between tokens, but not always • My dog • Mydog • I saw John, and said hello. • I saw John , and said hello • Automated by realiser

14. Pouring • Often want to insert line breaks to make text fit into a page of given width • Breaks should go between words if possible • Breaks should go between words if poss ible • If not possible, break between syllables and add a hyphen • Realiser automates

15. Output formatting • Many possible output formats • Simple text • HTML • RTF (MS Word) • Realiser can automatically add appropriate markups for this

16. Summary • Realiser automates the finicky details of language • So NLG developer doesn’t have to worry about these • One of the advantages of NLG

17. simplenlg classes • Realiser – realise objects • SyntaxPhraseSpec • SPhraseSpec – represents a sentence as verb, subjects, complements • NPPhraseSpec – represents a noun phrase as determiner, modifiers, noun • can be subject or complement in SPhraseSpec • PPPhraseSpec – represents prepositional phrase • Can be modifier in SPhraseSpec, NPPhraseSpec

18. Realiser API class Realiser { public void Realiser (); // default realiser (text) public void Realiser (boolean HTML); // HTML output ? public String realiser(Object spec); } Usage // create text spec ts Realiser r = new Realiser(); String output = r.realise(ts);

19. SPhraseSpec example SPhraseSpec p = new SPhraseSpec(); p.addSubject("my dog"); p.addSubject("your cat"); p.addVerb("like"); p.addComplement("balls"); p.setTense(SPhraseSpec.Tense.FUTURE); p.setNegated(true); Results in “My dog and your cat will not like balls.”

20. NPPhraseSpec example NPPhraseSpec subject = new NPPhraseSpec("dog"); subject.addModifier("big"); subject.addDeterminer("the"); Results in the big dog

21. PPhraseSpec Example PPPhraseSpec pp = new PPPhraseSpec("in", "the park")); p = new SPhraseSpec("I", "be"); p.addModifier(pp); Results in I am in the park

22. Lexicon • Lexicon class allows you to directly do morphology • Usually don’t need to call explicitly

23. Lexicon example Lexicon lex = new Lexicon(); System.out.println(lex.getPlural("child")); System.out.println(lex.getPast("eat")); System.out.println(lex.getPastParticiple("eat")); System.out.println(lex.getSuperlative("happy")); This will print out children ate eaten happiest

24. Other feature: Lexicaliser • Lexicaliser (different from lexicon) allows you to specify templates which produce SPhraseSpec from Protégé instances. • Won’t discuss here, see doc

25. Simplenlg • Being developed, currently working in • Referring expressions • Better documentation • Let me know if you have any comments or suggestions!