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‎.gitignore

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*.py[cod]
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# C extensions
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*.so
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# Packages
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*.egg
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*.egg-info
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dist
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build
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eggs
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parts
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bin
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var
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sdist
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develop-eggs
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.installed.cfg
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lib
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lib64
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# Installer logs
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pip-log.txt
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# Unit test / coverage reports
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.coverage
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.tox
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nosetests.xml
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# Translations
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*.mo
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# Mr Developer
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.mr.developer.cfg
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.project
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.pydevproject
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*~
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\#*\#
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/.emacs.desktop
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/.emacs.desktop.lock
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.elc
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auto-save-list
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tramp
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.\#*
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# Org-mode
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.org-id-locations
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*_archive
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# Trial temp
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_trial_temp

‎LICENSE

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‎README.md

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talon
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=====
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Mailgun library to extract message quotations and signatures.
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If you ever tried to parse message quotations or signatures you know that absense of any formatting standards in this area
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could make this task a nightmare. Hopefully this library will make your life much eathier. The name of the project is
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inspired by TALON - multipurpose robot designed to perform missions ranging from reconnaissance to combat and operate in
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a number of hostile environments. That's what a good quotations and signature parser should be like :smile:
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Usage
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-----
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Here's how you initialize the library and extract a reply from a text message:
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```python
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import talon
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from talon import quotations
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talon.init()
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text = """Reply
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-----Original Message-----
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Quote"""
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reply = quotations.extract_from(text, 'text/plain')
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reply = quotations.extract_from_plain(text)
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# reply == "Reply"
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```
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To extract a reply from html:
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```python
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html = """Reply
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<blockquote>
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<div>
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On 11-Apr-2011, at 6:54 PM, Bob &lt;[email protected]&gt; wrote:
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</div>
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<div>
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Quote
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</div>
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</blockquote>"""
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reply = quotations.extract_from(html, 'text/html')
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reply = quotations.extract_from_html(html)
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# reply == "<html><body><p>Reply</p></body></html>"
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```
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Often the best way is the easiest one. Here's how you can extract signature from email message without any
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machine learning fancy stuff:
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```python
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from talon.signature.bruteforce import extract_signature
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message = """Wow. Awesome!
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--
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Bob Smith"""
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text, signature = extract_signature(message)
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# text == "Wow. Awesome!"
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# signature == "--\nBob Smith"
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```
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Quick and works like a charm 90% of the time. For other 10% you can use the power of machine learning algorithms:
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```python
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from talon import signature
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message = """Thanks Sasha, I can't go any higher and is why I limited it to the
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homepage.
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John Doe
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via mobile"""
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text, signature = signature.extract(message, sender='[email protected]')
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# text == "Thanks Sasha, I can't go any higher and is why I limited it to the\nhomepage."
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# signature == "John Doe\nvia mobile"
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```
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For machine learning talon currently uses [PyML](http://pyml.sourceforge.net/) library to build SVM classifiers. The core of machine learning algorithm lays in ``talon.signature.learning package``. It defines a set of features to apply to a message (``featurespace.py``), how data sets are built (``dataset.py``), classifier's interface (``classifier.py``).
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The data used for training is taken from our personal email conversations and from [ENRON](https://www.cs.cmu.edu/~enron/) dataset. As a result of applying our set of features to the dataset we provide files ``classifier`` and ``train.data`` that don't have any personal information but could be used to load trained classifier. Those files should be regenerated every time the feature/data set is changed.
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Research
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--------
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The library is inspired by the following research papers and projects:
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* http://www.cs.cmu.edu/~vitor/papers/sigFilePaper_finalversion.pdf
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* http://www.cs.cornell.edu/people/tj/publications/joachims_01a.pdf

‎setup.cfg

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[metadata]
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description-file = README.md

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