Syntax

1. Syntax • Why is the structure of language (syntax) important? • How do we represent syntax? • What does an example grammar for English look like? • What strategies exist to find the structure in natural language? • A Prolog program to recognise English sentences

2. Pattern matching as an alternative (eg. Eliza) • This uses a database of input output pairs. • The input part of pair is a template to be matched against the user input • The output part of the pair is given as a response. • X computers Y => Do computers interest you? • X mother Y => Tell me more about your family?But… • Nothing is known about structure (syntax) • I X you => Why do you X me? • Fine for X = like, but not for X = do not know • Nothing is known about meaning (semantics) • I feel X => I'm sorry you feel X. • Fine for X = depressed, but not for X = happy

3. Syntax shows the role of words in a sentence. • John hit Suevs • Sue hit John • Here knowing the subject allows us to know what is going on.

4. Syntax shows how words are related in a sentence. • Visiting aunts ARE boring. • vs • Visiting aunts IS boring. • Subject verb agreement allows us to disambiguate here.

5. Syntax shows how words are related between sentences. • (a) Italy was beating England. Germany too. • (b) Italy was being beaten by England. Germany too. • Here missing parts of a sentence does not allow us to understand the second sentence. • But syntax allows us to see what is missing.

6. But syntax alone is not enough • Visiting museums can be boring • This is not ambiguous for us, as we know there is no such thing as a "visiting museum", but syntax cannot show this to a computer. • Compare with… • Visiting aunts can be boring

7. How do we represent syntax? • Parse Tree

8. How do we represent syntax? • List • Sue hit John • [ s, [np, [proper_noun, Sue] ] , • [vp, [v, hit], • [np, [proper_noun, John] ]

9. What does an example grammar for English look like? • Re-write rules • sentence -> noun phrase , verb phrase • noun phrase -> art , noun • noun phrase -> art , adj , noun • verb phrase -> verb • verb phrase -> verb , noun phrase

10. Chomsky Hierarchy • 0 Unrestricted A   • 1 Context-Sensitive | LHS |  | RHS | • 2 Context-Free |LHS | = 1 • 3 Regular |RHS| = 1 or 2 , A  a | aB | Ba

11. What Makes a Good Grammar? • Generality • Selectivity • Understandability

12. Generality of Grammars • Regular • {abd, ad, bcd, b, abcd, …} • S -> a S1 | b S2 | c S3 | d • S1 -> b S2 | c S3 | d • S2 -> c S3 | d • S3 -> d • Context Free • {anbn} • S -> ab | a S b • Context Sensetive • { anbncn} or {abcddabcdd, abab, asease, …}

13. Matching Constituents • * I ate hamburger and on the Stove • * I ate a cold hot dog and well burned • * I ate hot dog slowly and a hamburger • John hitting of Mary alarmed Sue • I cannot explain John hitting of Mary • Sue was alarmed by John hitting of Mary

14. Testing Constituents • I looked up John’s phone number • I looked up John’s chimney • * I looked up John’s phone number and in the cupboard • I looked up John’s chimney and in his cupboard • * up John’s phone number, I looked • up john’s chimney, I looked

15. An example: • Parsing sentence: • "They are cooking apples."

16. Parse 1

17. Parse 2

18. What strategies exist for trying to find the structure in natural language? • Top Down vs. Bottom Up

19. Top down parsing • 1 The 2 dog 3 cried 4 • Step Current state Backup States comment • 1 ((S) 1) initial position • 2 ((NP VP) 1) Rule 1 • 3 ((ART N VP) 1) Rules 2 & 3 • ((ART ADJ N VP) 1) • 4 ((N VP) 2) Match Art with the • ((ART ADJ N VP) 1) • 5 ((VP) 3) Match N with dog • ((ART ADJ N VP) 1) • 6 ((V) 3) Rules 5 & 6 • ((V NP) 3) • ((ART ADJ N VP) 1) • 7 Success

20. Parsing as a search procedure • Select the first state from the possibilities list (and remove it from the list). • Generate the new states by trying every possible option from the selected state (there may be none if we are on a bad path). • Add the states generated in step 2 to the possibilities list

21. What strategies exist for trying to find the structure in natural language? • Depth First vs. Breadth First

22. An Example of Top-Down Parsing1 The 2 old 3 man 4 cried 5

23. Depth First Search versus Breadth First

24. What strategies exist for trying to find the structure in natural language? • Left - Right vs. Right – Left • Left - Right • Take words from left to right • Take rule constituents from left to right • Right - Left • Take words from right to left • Take rule constituents from right to left • Left - Right usually best for a language like English • where subject comes before verb ; good for subject - verb agreement; speech & real time input is L->R; closer to human processing. • We have trouble with "Have the students given their assignments by their lecturers" for this reason.

25. What strategies exist for trying to find the structure in natural language? • A simple Prolog parser's strategy • Top Down • Prolog tries to satisfy the query "Is this a sentence?" and works top down in a search for resolution of this query. • Depth first • Prolog's in built search strategy is depth first, so a simple parser uses this. • L->R • The grammar rules are taken from left to right, and so are the words of phrases / sentences as Prolog tries to match them against the grammar rules.

26. What does a Prolog program look like that tries to recognise English sentences? • s --> np vp. • np --> det n. • np --> det adj n. • vp --> v np.

27. What does a Prolog program look like that tries to recognise English sentences? • sentence(S) :- • noun_phrase(NP), verb_phrase(VP), append(NP,VP,S). • noun_phrase(NP) :- • determiner(D),noun(N),append(D,N,NP). • noun_phrase(NP) :- • determiner(D),adj(A),noun(N),append(D,A,AP),append(AP,N,NP). • verb_phrase(VP) :- • verb(V), noun_phrase(NP), append(V,NP,VP). • determiner([D]) :- member(D,[the,a,an]). • noun([N]) :- member(N,[cat,dog,mat,meat,fish]). • adj([A]) :- member(A,[big,fat,red]). • verb([V]) :- member(V,[ate,saw,killed,pushed]).