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Regular Expression based parsers for extracting data from natural languages

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Reparse

Python library/tools for combining and parsing using Regular Expressions in a maintainable way

[Build Status Download/View Source on Github] [Docs at ReadtheDocs]

This library also allows you to:

  • Maintain a database of Regular Expressions
  • Combine them together using Patterns
  • Search, Parse and Output data matched by combined Regex using Python functions.

This library basically just gives you a way to combine Regular Expressions together and hook them up to some callback functions in Python.

A Taste / Getting Started

(See the examples/ directory for a full code examples)

Say your fashionista friend must know what colors their friends like at certain times. Luckily for you two, your friend's friends are blogging fanatics and you have downloaded thousands of text documents containing their every thought.

So you want to get (color and time) or [('green', datetime.time(23, 0))] out of text like:

blah blah blah go to the store to buy green at 11pm! blah blah

If you need scan/search/parse/transform some unstructured input and get some semi-structured data out of it Reparse might be able to help.

First structure some Regular Expressions (Here, in Yaml)

Color:
    Basic Color:
        Expression: (Red|Orange|Yellow|Green|Blue|Violet|Brown|Black)
        Matches: Orange | Green
        Non-Matches: White
        Groups:
          - Color

Time:
    Basic Time:
        Expression: ([0-9]|[1][0-2]) \s? (am|pm)
        Matches: 8am | 3 pm
        Non-Matches: 8a | 8:00 am | 13pm
        Groups:
          - Hour
          - AMPM

Then structure some Patterns with those expressions (Yaml)

BasicColorTime:
  Order: 1
  Pattern: |
    <Color> \s? at \s? <Time>
  # Angle brackets detonate expression groups
  # Multiple expressions in one group are combined together

Some callback functions (in Python)

from datetime import time
def color_time(Color=None, Time=None):
    Color, Hour, Period = Color[0], int(Time[0]), Time[1]
    if Period == 'pm':
        Hour += 12
    Time = time(hour=Hour)

    return Color, Time

Build your parser

from examples.colortime.functions import functions
import reparse


colortime_parser = reparse.parser(
    parser_type=reparse.basic_parser,
    expressions_yaml_path=path + "expressions.yaml",
    patterns_yaml_path=path + "patterns.yaml",
    functions=functions
)

print(colortime_parser("~ ~ ~ go to the store ~ buy green at 11pm! ~ ~"))

Result

[('green', datetime.time(23, 0))]

Cool!

Intrigued? Learn more how to make the magic happen in Howto: How to use Reparse.

Want to read more about what Reparse is and what it can do? More info in About: Why another tool for parsing?

Info

Installation

pip

pip install reparse

manually

  1. If you don't have them already, Reparse depends on REGEX, and PyYaml. Download those and python setup.py install in their directories. If you are on windows, you may have to find binary installers for these, since they contain modules that have to be compiled.
  2. Download the Zip off of Github (or clone the repo).
  3. Extract and do python setup.py install in the reparse containing the setup.py file directory. You can also just have the reparse/reparse directory in the source tree of your project if you don't want to install it.
  4. Test with python -c "import reparse", no output means it is probably installed. If you get ImportError: No module named reparse then you might want to recheck your steps.

Support

Need some help? Send me an email at theandychase@gmail.com and I'll do my best to help you.

Contribution

The code is located on Github. Send me suggestions, issues, and pull requests and I'll gladly review them!

Versions

  • 3.0 InvalidPattern Exception, Allow monkey patching regex arguments. RE|PARSE -> Reparse.
  • 2.1 Change yaml.load to yaml.safe_load for security
  • 2.0 Major Refactor, Python 3, Better Parser builders
  • 1.1 Fix setup.py
  • 1.0 Release

Licence

MIT Licensed! See LICENSE file for the full text.

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Regular Expression based parsers for extracting data from natural languages

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