ACCL

Phase 1: Simply use python nltk to find the similarity between words for highlighting.

Phase 2: Use Wikipedia based, nltk assisted similarity && look up mechanism for highlighting keywords.

Functionality: Accelerate read speed by converting input text to html page with keywords highlighted.

Why using it: This tool will help you to better skim through a long article/paper/essay using automatic keyword highlighting mechanisms.

How to use it:

In Phase 2:

          First, currently with not perfect UI, clone this repository, have Python 2.7, 

          download nltk (www.nltk.org) and wikipedia (https://github.com/goldsmith/Wikipedia) module,
          
          Then substitute file_env to the keywords you know are key words a before reading. (1 line style)
          
          Then substitute file_in to the paragraph you want to highlight.
          
          Then in command line/ terminal, type: python acc_Learn_wiki.py
          
          Then you can open the output file file_out.html in browser.

Demo:

          Get the oxygen page on wikipedia and put its first 3 paragraphs in my file_in.txt.

          Then put word "oxygen" in my file_env,

          Then my keyword highlighting mechanism think the phrases ["Chemical element","ozone layer","Earth's crust","Ultraviolet radiation","Cellular respiration","oxygen","periodic table","Great oxygenation event","Billion years","Earth's atmosphere","Electrolysis of water","Atomic number","Fractional distillation","Life on Earth","Earth","Life Support System","Billion years ago","Low earth orbit"]

          Then render html with those words highlighted red!

Current Constraints and further TODOs:

1.Constraint 1:Currently only accept English. For example: greek will break it.

2.Constraint 2 && TODO1 :Bad UI, not that easy to use.

3.TODO2: Change input format from txt to more common formats such as pdf.