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