3.1 work package list
Every task is in  my program one function.

Workpackage 1:Pure text

  Objective:  to extract the pure text from penn tree bank 
     
Firstly we need to extract only the text from penn tree bank ,and at the same time we can get all the position information from this process.

Task 1.1 Cleaning

removes not necessary whitespace and newline in every sentence and the explanations at the beginn of the file

Task 1.2 Extracting

to extract all the terminal(text) aut of the Treebank

Task 1.3 refining

to clean those 's 'm 'n't 'd 're problems

Workpackage 2:Counting 

  Objectiv : to count the position information and put it into the penntree bank

Inorder to get the position information one must parse the tree bank

Task 2.1 Terminal

to count the positions of the terminals 

task 2.2 Printing

print all the phrases with the postion informations 

Task 2.3 Removing

removes all the teminal position informations so that later we
can use the recursive program to extract the real entity
removes the additional information in infile.NPDEPTH

Workpackage 3: Main function

Objectiv:to get rid of the phrases that we do not need 

Task 3.1 Extracting

Objective:to get the information of the first subknotes of a NP which we need 

For example: (NP (NP different parts) (PP of (NP Europe))) 
They want to mark both ``different parts of Europe'' and ``Europe'', since they both contribute distinct DE's. How­ ever, notice that ``different parts'' does not contribute a DE since it is not possible to refer to this subexpression alone in subsequent discourse. To avoid finding such undesirable NP's, our system has a heuristic (H1) which says: Pass over any NP which is a leftmost child of a top­level NP. This heuristic is too drastic, though, eliminating constructions like (2). (2) (NP (NP the inner brain) and (NP the eyes)) 
To avoid losing these examples, they include another heursitic (H2) which says: H1 does not apply when the NP is a sibling of another NP. A third heuristic must be added to overrule H1 in the case of a possessor in a possessive construction, such as: 
(3) (NP (NP Chicago's) South Side) where they should extract both ``Chicago'' and ``Chicago's South Side''. So, the heuristic H3 is introduced: H1 does not apply when the NP is a possessive form. Even with heuristics eliminating the NP's which they 
do not need to consider, there are some NP's that will be found by the system which cannot be eliminated automatically. Copular constructions such as (4) introduce unnecessary NP's. 

task 3.2 NP problem

get the NP phrases out of all the phrases and extract the begin and
end positions of each phrase and add it at the end of each phrase
we need

task 3.3 NP-PRD problem

objective :get rid of the NP in NP-PRD that we don't need produce the list1
in which the index of phrases that we don't need
Workpackage 4: some execptions and test

For example:``John'' and ``a doctor'' are syntactically NP's, but the second does not contribute a unique DE. 

Workpackage 4:test

Task4.1 NP-PRD execption

 for the NP-PRD we should get rid of all the sonknote of the first 
subknote and also if there is a , , in NP-PRD the first knote after
it muss also be deleted

Task 4.2 Extraction

 get rid of all the phrases we don't need

Task 4.3 Final

to give all the np-extraction from Penn Treebank as the input for 
the tool Referee
task 4.4 Test

3.2 list of deliverables

Workpackage 1 
	Pure text
	January 1 – February 1	
Workpackage 2 
	Counting	February 1 – April 1	
Workpackage 3 
	Main function	April 1 – Juli 1	
Workpackage 4 
	test	Juli 1 – October 1