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Description
We will focus on following improvements:
- Data collection
Transfer data from PDF to text files.
- Text extraction
Extract text contents from the documents.
- Sentence tokenizing
Remove stop words, group sentences with similar meanings, and mark groups with certain labels.
- Classification of category and subcategories
Classify each group based on meaning of sentences.
5. Interface for easily indicating the location of files
Implementation Method:
Tools/Platforms: Anaconda(Jupyter), SPSS Modeler
Python Packages: NLTK, pdfminer3k, Scikit Learn, etc
Main challenges:
Building proper dictionary
Classifying the policies
Making user-friendliness interface
Timeline:
- Before February 28th: Idea
- Before March 10th: Data collection & Methodology & Tools choosing
- Before April 25th: Realization & Results
- Before May 3th: Improvements
- Final results & presentation
Expected Outcomes:
Building a user-friendliness dictionary
Co-authors to your solution
Zijing Yu, Qinruo Wu, Zihao Wang
Link to your concept design and documentation (Required by the final day of the Submission & Collaboration phase)
Link to an online working solution or prototype (Required by the final day of the Submission & Collaboration phase):
Link to a video or screencast of your solution or prototype (Required by the final day of the Submission & Collaboration phase):
Link to source code of your solution or prototype above. (If you submitted a link to an online solution or prototype, or to a video of your solution of prototype, you must provide a link to the source code. This item is required by the final day of the submission phase):
Cybersecurity,Classification,Tokenization,Interface,Text Analytics,Data Extraction
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