115 lines
3.3 KiB
ReStructuredText
115 lines
3.3 KiB
ReStructuredText
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 absence of any formatting standards in this area could make this task a nightmare. Hopefully this library will make your life much easier. 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’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
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message:
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.. code:: 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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To extract a reply from html:
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.. code:: 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 <bob@example.com> 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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Often the best way is the easiest one. Here’s how you can extract
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signature from email message without any
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machine learning fancy stuff:
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.. code:: 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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Quick and works like a charm 90% of the time. For other 10% you can use
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the power of machine learning algorithms:
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.. code:: python
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import talon
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# don't forget to init the library first
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# it loads machine learning classifiers
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talon.init()
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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='john.doe@example.com')
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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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For machine learning talon currently uses `PyML`_ library to build SVM
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classifiers. The core of machine learning algorithm lays in
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``talon.signature.learning package``. It defines a set of features to
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apply to a message (``featurespace.py``), how data sets are built
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(``dataset.py``), classifier’s interface (``classifier.py``).
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The data used for training is taken from our personal email
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conversations and from `ENRON`_ dataset. As a result of applying our set
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of features to the dataset we provide files ``classifier`` and
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``train.data`` that don’t have any personal information but could be
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used to load trained classifier. Those files should be regenerated every
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time the feature/data set is changed.
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.. _PyML: http://pyml.sourceforge.net/
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.. _ENRON: https://www.cs.cmu.edu/~enron/
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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
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