1. Make a file 'hindicorpus.txt' that contains the merged hindi corpus from Heliohost source and CFILT. This should be a single file. This file is not included here as it is pretty big ~ 250MB.

2. Run Makefile by typing 'make' in terminal in the folder with codes.
	-This will clean the corpus of special characters. 
	-Write in the format, frequency word
	-Remove words with frequency less than 2.
	-Remove words with less than 3 characters.
	
	The output of this execution will be 'trainhindicorpus.txt'. This is the final pruned training corpus for Undivided++.

3. Compile the Undivided++ code by the following command. 
	- g++ UnsupervisedWordSegmentation.cpp

4. Run the code using the following command. 
	- ./a.out trainhindicorpus.txt 1 1 0 1 0

5. The test corpus given to me  (list04.txt) is renamed as testcorpus.txt. Run the python script named 'make_test_corpus.py' 
	- python make_test_corpus.py > testcorpusfinal.txt
	- This removes the frequency component from each line.
	
6. Run merge.py script using 
	- python merge.py > finalresult.txt
	
	The file finalresult.txt contains morphological segmentation of the words in the test corpus as done my Undivided++.
	 
