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6320.501: Natural Language Processing
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Author Name: Dhwani Raval
Project Name: Brill's Tagger
Version: 0.1
Programming Language: Python 3.6


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Problem Description
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Transformation-based POS Tagging: Implement Brill’s transformation-based POS tagging algorithm using ONLY the previous
word’s tag to extract the best five (5) transformation rules to:
1. Transform “NN” to “VB”
2. Transform “VB” to “NN”
Using the learnt rules, manually fill out the missing POS tags (for the word “control”) in the following sentence:
The_DT president_NN wants_VBZ to_TO control_??? the_DT board_NN 's_POS control_???


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About Files
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The project contains the following files:
    1. sourcecode/Tagger.py: The python file for the given problem description
    2. resources/POSTaggedTrainingSet.txt: A training set that has been tagged with POS tags from the Penn Treebank POS
       tagset
    3. output/tuple: A text file created during program execution
    4. output/unigram: Text files related to unigrams created during program execution
    5. output/tags: Text files related to correct and current tags for words created during program execution
    6. output/logs: Log files created during each iteration for top 10 rules
    7. output/top10.txt: Top 10 transformation rules
    8. readme: A text file containing information about the project


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Brill's Tagging Description
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Brill's Tagging is used for Part-of-Speech (POS) tagging. It is inductive in nature and is based on
Transformation Based Learning (TBL). The basic idea is to iteratively assign the best tag to a word
using the learned transformations based upon a set of predefined rules. The goal of this approach is to
minimize the error rate in every step.


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Running Instructions
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1. Download the project and unzip it in the desired location
2. In IDE, import the project and run Tagger.py
3. In cmd, navigate to the location where Brill_Tagging is unzipped and use the following instruction
    python sourcecode\Tagger.py