Subject Code : IFN647
Assignment Task:

Question 1. Design Python code for text pre-processing 
(a) Parsing and tokenizing - read files from RCV1v2, find the documentID and record it to a collection of BowDocument Objects. 
• The documentID is simply assigned by the ‘itemid’ in <newsitem> 
• In this task, the created BowDocument can be initialled with found documentID and an empty dictionary of key-value pair of (String term: int frequency). 
• Build up a collection of BowDocument for the given dataset, this collection can be a dictionary structure (a linked list or other data structure. Please note the rest descriptions are based on the dictionary structure) with documentID as key and BowDocument object as value. 
• Create a method (or function) to print out all documentIDs by iterating above collection and calling BowDocument’s method getDocId(). 
• Tokenizing – fill term:freq dictionary for each document. 

  • You only need to tokenize the ‘<text>...</text>’ part of document, exclude all tags, and discard punctuations and numbers. 
  •  Define addTerm() of BowDocument to add new term or increase term frequency when the term occur again. 
  •  Create a method displayDocInfo() to display term list with a given docuemntID. The output should be like: 

 Doc docId has termCount different terms: 
Term1, 3 
Term2, 1 
Term3, 4 
.... 
(b) Stopping words removal and stemming of terms – use given stopping words list (file “common-english-words.txt” in Week 3 workshop) to ignore/remove all stopping words from the term list of documents, and use porter2 stemming algorithm to update BowDocument’s term list (e.g., the Dictionary) 
• Update your program to read in given stopping words list and store to a list stopWordsList. 
• Update your program, when adding term, check the term if or not exist in stopping words list, ignore such term if it is in. 
• Call the method displayDocInfo() again. 
(c) Sort and display document term:freq list by frequency. You may do this after you have finished and passed all the above 3 steps, and save the output into a text file (file name is “your full name_Q1.txt”)

Question 2. Design Python code to calculate tf*idf weights 
(a) Calculate document-frequency (df) for each term and store them in a term:df dictionary. Call the created function (or method) then display a list of term:df pairs for the whole RCV1v2 document collection, and save the output into a text file (file name is “your full name_Q2a.txt”). 

 

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  • Uploaded By : Alex Cerry
  • Posted on : June 04th, 2019

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