1. Methods in Computational Linguistics II Queens College Lecture 8: Dynamic Programming

2. Today Inheritance Recursion Dynamic Programming Machine Learning Primer 1

3. Inheritance Objects can define relationships between objects. Membership operations allow for the representation of “has-a”, “has-many”, and arbitrary property relationships. Inheritance relationships allow for the representation of “is-a” relationships. 2

4. Subclasses and Inheritance class Base(object): def __init__(self): print “Base init” class Derived(Base): def __init__(self): super(Derived, self).__init__() 3

5. Shape Example 4 Shape Rectangle Triangle Circle Square

6. Trees Binary Trees are a commonly used data structure. The core element of a tree is a node These nodes can contain values, and include pointers to one or more children, that are differentiated as “left” and “right” 5

7. Tree Example 6 5 5 a a xyz 15 The

8. Binary Search Trees Binary Search trees have the properties –the value (or key), of any node is greater than the value of its left child –the value of any node is less than the value of its right child. 7

9. Binary Search Tree Example 8 5 5 2 2 4 4 1 1 9 9 What does this have to do with Binary Search?

10. Tree class What does a tree class require? 9

11. Graphs Graphs are similar to Trees Graphs have: –Nodes –Edges Nodes can contain values Edges connect two notes, and can also have a value. 10

12. Graph Example 11 5 5 2 2 4 4 1 1 9 9 a a b c c d

13. What does a Graph class require? 12

14. In class coding Using an object in a program Initializing data in an object Objects that you can iterate over Look at the Shape class Look at the Tree class 13

15. Recursion A function that calls itself is called a recursive function. A good recursive function must include –A stopping condition –Modification 14

16. Example of Recursion Print every element of a tree. Search for an entry in a tree. Print every element of a graph. Search in a graph. 15

17. Dynamic Programming Fibonacci Numbers. F(x) = F(x-1) + F(x-2) F(2) = 1 F(1) = 1 16

18. Recursive Fibonacci F(6) = F(5) + F(4) F(5) = F(4) + F(3) F(4) = F(3) + F(2) F(3) = F(2) + F(1) F(4) = F(3) + F(2) F(3) = F(2) + F(1) F(4) = F(3) + F(2) F(3) = F(2) + F(1) 17

19. Or do it the smart way F(1) = 1 F(2) = 1 F(3) = F(2) + F(1) = 2 F(4) = F(3) + F(2) = 3 F(5) = F(4) + F(3) = 5 F(6) = F(5) + F(4) = 8 18

20. Dynamic Programming Optimal Substructure Repeated Subproblems 19

21. Viterbi Decoding We know how likely we are to be in a state. We know how likely we are to transition from one state to another. Find the best state sequence. 20

22. Lattice 21 Every node is is labeled with P(x|k) Every edge is labeled with a transition probability a ij

23. Next Time Machine Learning –A primer on machine learning and its role in Computational Linguistics. –Classification in NLTK (after break) 22