98 lines
3.0 KiB
Markdown
98 lines
3.0 KiB
Markdown
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's what a good quotations and signature parser should be like :smile:
|
|
|
|
Usage
|
|
-----
|
|
|
|
Here's how you initialize the library and extract a reply from a text message:
|
|
|
|
```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:
|
|
|
|
```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>"
|
|
```
|
|
|
|
Often the best way is the easiest one. Here's how you can extract signature from email message without any
|
|
machine learning fancy stuff:
|
|
|
|
```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:
|
|
|
|
```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](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``).
|
|
|
|
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.
|
|
|
|
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
|