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ctparse 0.3.6

ctparse - Parse natural language time expressions in python

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
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
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
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:
@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:
@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:

Input a string and a reference time
Find all matches of all regular expressions from all rules in the
input strings. Each regular expression is assigned an identifier.
Find all distinct sequences of these matches where two matches do not
overlap nor have a gap inbetween
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
PCFG 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.

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.