ctparse 0.3.6
Parse natural language time expressions in python
License: MIT license
Keywords: ctparse time parsing natural language

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ctparse - Parse natural language time expressions in python
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* Free software: MIT license
* Documentation: https://ctparse.readthedocs.io.


Background
----------

The package ``ctparse`` is a pure python package to parse time
expressions from natural language (i.e. strings). In many ways it builds
on similar concepts as Facebook’s ``duckling`` package
(https://github.com/facebook/duckling). However, for the time being it
only targets times and only German and English text.

In principle ``ctparse`` can be used to **detect** time expressions in a
text, however its main use case is the semantic interpretation of such
expressions. Detecting time expressions in the first place can - to our
experience - be done more efficiently (and precisely) using e.g. CRFs or
other models targeted at this specific task.

``ctparse`` is designed with the use case in mind where interpretation
of time expressions is done under the following assumptions:

-  All expressions are relative to some pre-defined reference times
-  Unless explicitly specified in the time expression, valid resolutions
   are in the future relative to the reference time (i.e. ``12.5.`` will
   be the next 12th of May, but ``12.5.2012`` should correctly resolve
   to the 12th of May 2012).
-  If in doubt, resolutions in the near future are more likely than
   resolutions in the far future (not implemented yet, but any
   resolution more than i.e. 3 month in the future is extremely
   unlikely).

The specific comtravo use-case is resolving time expressions in booking
requests which almost always refer to some point in time within the next
4-8 weeks.

``ctparse`` currently is language agnostic and supports German and
English expressions. This might get an extension in the future. The main
reason is that in real world communication more often than not people
write in one language (their business language) but use constructs to
express times that are based on their mother tongue and/or what they
believe to be the way to express dates in the target language. This
leads to text in German with English time expressions and vice-versa.
Using a language detection upfront on the complete original text is for
obvious no solution - rather it would make the problem worse.

Example
-------

.. code:: python

   from ctparse import ctparse
   from datetime import datetime

   # Set reference time
   ts = datetime(2018, 3, 12, 14, 30)
   ctparse('May 5th 2:30 in the afternoon', ts=ts)

This should return a ``Time`` object represented as
``Time[0-29]{2018-05-05 14:30 (X/X)}``, indicating that characters
``0-29`` were used in the resolution, that the resolved date time is the
5th of May 2018 at 14:30 and that this resolution is neither based on a
day of week (first ``X``) nor a part of day (second ``X``).


Latent time
~~~~~~~~~~~

Normally, ``ctparse`` will anchor time expressions to the reference time.
For example, when parsing the time expression ``8:00 pm``, ctparse will
resolve the expression to 8 pm after the reference time as follows

.. code:: python

   parse = ctparse("8:00 pm", ts=datetime(2020, 1, 1, 7, 0), latent_time=True) # default
   # parse.resolution -> Time(2020, 1, 1, 20, 00)

This behavior can be customized using the option ``latent_time=False``, which will
return a time resolution not anchored to a particular date

.. code:: python

   parse = ctparse("8:00 pm", ts=datetime(2020, 1, 1, 7, 0), latent_time=False)
   # parse.resolution -> Time(None, None, None, 20, 00)

Implementation
--------------

``ctparse`` - as ``duckling`` - is a mixture of a rule and regular
expression based system + some probabilistic modeling. In this sense it
resembles a PCFG.

Rules
~~~~~

At the core ``ctparse`` is a collection of production rules over
sequences of regular expressions and (intermediate) productions.

Productions are either of type ``Time``, ``Interval`` or ``Duration`` and can
have certain predicates (e.g. whether a ``Time`` is a part of day like
``'afternoon'``).

A typical rule than looks like this:

.. code:: python

   @rule(predicate('isDate'), dimension(Interval))

I.e. this rule is applicable when the intermediate production resulted
in something that has a date, followed by something that is in interval
(like e.g. in ``'May 5th 9-10'``).

The actual production is a python function with the following signature:

.. code:: python

   @rule(predicate('isDate'), dimension(Interval))
   def ruleDateInterval(ts, d, i):
     """
     param ts: datetime - the current refenrence time
     d: Time - a time that contains at least a full date
     i: Interval - some Interval
     """
     if not (i.t_from.isTOD and i.t_to.isTOD):
       return None
     return Interval(
       t_from=Time(year=d.year, month=d.month, day=d.day,
                   hour=i.t_from.hour, minute=i.t_from.minute),
       t_to=Time(year=d.year, month=d.month, day=d.day,
                 hour=i.t_to.hour, minute=i.t_to.minute))

This production will return a new interval at the date of
``predicate('isDate')`` spanning the time coded in
``dimension(Interval)``. If the latter does code for something else than
a time of day (TOD), no production is returned, e.g. the rule matched
but failed.


Technical Background
~~~~~~~~~~~~~~~~~~~~

Some observations on the problem:

-  Each rule is a combination of regular expressions and productions.
-  Consequently, each production must originate in a sequence of regular
   expressions that must have matched (parts of) the text.
-  Hence, only subsequence of **all** regular expressions in **all**
   rules can lead to a successful production.

To this end the algorithm proceeds as follows:

1. Input a string and a reference time
2. Find all matches of all regular expressions from all rules in the
   input strings. Each regular expression is assigned an identifier.
3. Find all distinct sequences of these matches where two matches do not
   overlap nor have a gap inbetween
4. To each such subsequence apply all rules at all possible positions
   until no further rules can be applied - in which case one solution is
   produced

Obviously, not all sequences of matching expressions and not all
sequences of rules applied on top lead to meaningful results. Here the
**P**\ CFG kicks in:

-  Based on example data (``corpus.py``) a model is calibrated to
   predict how likely a production is to lead to a/the correct result.
   Instead of doing a breadth first search, the most promising
   productions are applied first.
-  Resolutions are produced until there are no more resolutions or a
   timeout is hit.
-  Based on the same model from all resolutions the highest scoring is
   returned.


Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.3.4 (2022-07-28)
------------------

* Add fuzzy matching on longer literals
* [internal] De-tangle corpus tests into isolated test cases
* Allow spaces around separators in ruleDDMMYYYY and ruleYYYYMMDD

0.3.3 (2022-07-18)
------------------

* Add rule for straight forward US formatted dates (`ruleYYYYMMDD`)
* Added rule `ruleYearMonth`
* Added corpus cases for some open issues that now pass
* Changed all internal imports to be absolute (i.e. `from ctparse.x` instead of `from .x`)
* Dropped `tox` (now using github actions)

0.3.2 (2022-07-18)
------------------

* Drop support for python 3.6, update dev requirements

0.3.1 (2021-07-07)
------------------

* Add support for python 3.9 on travis and in manifest; update build config

0.3.0 (2021-02-01)
------------------

* Removed latent rules regarding times (latent rules regarding dates are still present)
* Added latent_time option to customize the new behavior, defauld behavior is backwards-compatible

0.2.1 (2020-05-27)
------------------

* Update development dependencies
* Add flake8-bugbear and fixed issues

0.2.0 (2020-04-23)
------------------

* Implemented new type `Duration`, to handle lengths of time
* Adapted the dataset to include `Duration`
* Implemented basic rule to merge `Duration`, `Time` and `Interval` in simple cases.
* Created a make target to train the model `make train`

0.1.0 (2020-03-20)
------------------

* Major refactor of code underlying predictive model
* Based on a contribution from @bharathi-srini: replace naive bayes from sklearn by own implementation
* Thus remove dependencies on numpy, scipy, scikit-learn
* Predictions are much faster: 97/s in the old vs. 239/s in the new code base
* Performance identical
* Deprecate support for python 3.5, add 3.8
* Add more strict type checking rules (mypy.ini)
* Force black code formatting, make this a linter step, "black" all code

0.0.47 (2020-02-28)
-------------------

* Allow overlapping matches of regular expression when generating inital stack of "tokens"

0.0.46 (2020-02-26)
-------------------

* Implemented heuristics to detect (albeit imperfectly) military times

0.0.44 (2019-11-05)
-------------------

* Released time corpus
* Implemented training model using ctparse corpus

0.0.43 (2019-11-01)
-------------------

* Added slash as a general separator
* Added ruleTODTOD (to support expression like afternoon/evening)

0.0.42 (2019-10-30)
-------------------

* Removed nb module
* Fix for two digit years
* Freshly retrained model binary file

0.0.41 (2019-10-29)
-------------------

* Fix run_corpus refactoring bug
* Implemented retraining utilities

0.0.40 (2019-10-25)
-------------------

* update develop dependencies
* remove unused Protocol import from typing_extensions

0.0.39 (2019-10-24)
-------------------

* split ctparse file into several different modules
* added types to public interface
* introduced the Scorer abstraction to implement richer scoring strategies

0.0.38 (2018-11-05)
-------------------

* Added python 3.7 to supported versions (fix on travis available)

0.0.8 (2018-06-07)
------------------

* First release on PyPI.