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 Sue vs 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 {a n b n } 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 1) I looked up John’s phone number 2) 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 Bottom - Up John, hit, the, cat prpn, hit, the, cat prpn, v, the, cat prpn, v, det, cat prpn, v, det, n np, v, det, n np, v, np np, vp s Better if many alternative rules for a phrase Worse if many alternative terminal symbols for each word Top - Down s s -> np, vp s -> prpn, vp s -> John, v, np s -> John, hit, np s -> John, hit, det,n s -> John, hit, the,n s -> John, hit, the,cat Better if many alternative terminal symbols for each word Worse if many alternative rules for a phrase

19. Top down parsing 1 The 2 dog 3 cried 4 Step Current stateBackup 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) 7Success

20. Parsing as a search procedure 1. Select the first state from the possibilities list (and remove it from the list). 2. Generate the new states by trying every possible option from the selected state (there may be none if we are on a bad path). 3. 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 Depth First Try rules one at a time and back track if you get stuck Easier to program Less memory required Good if parse tree is deep Breadth First Try all rules at the same time Can be faster Order of rules is not important Good if tree is flat

22. An Example of Top-Down Parsing 1 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),appen d(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]).