# Copyright 2024, Battelle Energy Alliance, LLC ALL RIGHTS RESERVED
# The code is adapted from https://github.com/wjbmattingly/date-spacy
# and https://github.com/AliSeyedkav/SMS-TEXT-Time-Date-Recognition-
import re
from spacy.tokens import Span
from spacy.language import Language
from spacy.util import filter_spans
from .SimpleEntityMatcher import SimpleEntityMatcher
# dateparse: python parser for human readable dates: https://dateparser.readthedocs.io/en/latest/
# May be adapted in the future
# import dateparser
# Set up a date extension on the span
# Span.set_extension("Temporal", default=None, force=True)
@Language.factory("Temporal")
[docs]
def find_temporal(nlp, name):
return Temporal(nlp)
[docs]
class Temporal(object):
"""
Temporal Entity Recognition class
How to use it:
.. code-block:: python
from TemporalEnity import Temporal
nlp = spacy.load("en_core_web_sm")
pmatcher = Temporal(nlp)
doc = nlp("The event is scheduled for 25th August 2023.")
updatedDoc = pmatcher(doc)
or:
.. code-block:: python
nlp.add_pipe('Temporal')
newDoc = nlp(doc.text)
"""
def __init__(self, nlp):
"""
Args:
nlp: spacy nlp model
"""
[docs]
self.ordinalToNumber = {
"first": "1", "second": "2", "third": "3", "fourth": "4", "fifth": "5",
"sixth": "6", "seventh": "7", "eighth": "8", "ninth": "9", "tenth": "10",
"eleventh": "11", "twelfth": "12", "thirteenth": "13", "fourteenth": "14",
"fifteenth": "15", "sixteenth": "16", "seventeenth": "17", "eighteenth": "18",
"nineteenth": "19", "twentieth": "20", "twenty-first": "21", "twenty-second": "22",
"twenty-third": "23", "twenty-fourth": "24", "twenty-fifth": "25", "twenty-sixth": "26",
"twenty-seventh": "27", "twenty-eighth": "28", "twenty-ninth": "29", "thirtieth": "30",
"thirty-first": "31"
}
# Ordinals
ordinals = [
"first", "second", "third", "fourth", "fifth",
"sixth", "seventh", "eighth", "ninth", "tenth",
"eleventh", "twelfth", "thirteenth", "fourteenth",
"fifteenth", "sixteenth", "seventeenth", "eighteenth",
"nineteenth", "twentieth", "twenty-first", "twenty-second",
"twenty-third", "twenty-fourth", "twenty-fifth", "twenty-sixth",
"twenty-seventh", "twenty-eighth", "twenty-ninth", "thirtieth", "thirty-first"
]
ordinalPattern = r"\b(?:" + "|".join(ordinals) + r")\b"
exceptions = [
"hour", "hours", "minute", "minutes", "day", "days", "decade", "decades", "century", "centuries", "week", "weeks", "month",
"months", "year", "years"
]
exceptionsPattern = r"(?:" + "|".join(exceptions) + r")\b"
# A regex pattern to capture a variety of date formats
[docs]
self.datePattern = r"""
# Day-Month-Year
(?:
\d{1,2}(?:st|nd|rd|th)? # Day with optional st, nd, rd, th suffix
\s+
(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
(?: # Year is optional
\s+
\d{4} # Year
)?
)
|
# Day/Month/Year
(?:
\d{1,2} # Day
[/-]
\d{1,2} # Month
(?: # Year is optional
[/-]
\d{2,4} # Year
)?
)
|
# Year-Month-Day
(?:
\d{4} # Year
[-/]
\d{1,2} # Month
[-/]
\d{1,2} # Day
)
|
# Month-Day-Year
(?:
(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
\s+
\d{1,2}(?:st|nd|rd|th)? # Day with optional st, nd, rd, th suffix
(?: # Year is optional
,?
\s+
\d{4} # Year
)?
)
|
# Month-Year
(?:
(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
\s+
\d{4} # Year
)
|
# Ordinal-Day-Month-Year
(?:
""" + ordinalPattern + r"""
\s+
(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
(?: # Year is optional
\s+
\d{4} # Year
)?
)
|
(?:
""" + ordinalPattern + r"""
\s+
of
\s+
(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
(?: # Year is optional
\s+
\d{4} # Year
)?
)
|
# Month Ordinal
(?:
(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
\s+
""" + ordinalPattern + r"""
(?: # Year is optional
\s+
\d{4} # Year
)?
)
|
(?:
\d+
(?:\-|\s+)?
""" + exceptionsPattern + """
)
"""
#
terms1 = [
"next", "last", "after", "every", "before", "during"
]
terms2 = [
"today", "tomorrow", "year", "Monday", "Tuesday", "Wednesday", "Thursday", "yesterday", "weekend",
"Friday", "Saturday", "Sunday"
]
terms3 = [
"morning", "afternoon", "noon", "dawn", "midnight", "dusk", "sunrise", "sunset", "evening", "night", "week", "weeks", "month", "months",
"year", "years"
]
pattern = [
[{"LOWER": {"in": terms1}, "OP": "?"}, {"LEMMA": {"in": terms2}, "OP": "+"}],
[{"LOWER": {"in": terms1}, "OP": "?"}, {"LEMMA": {"in": terms2}, "OP": "?"}, {"ENT_TYPE": {"in": ["DATE", "TIME"]}, "OP": "?"}],
[{"LOWER": {"in": ["at", "on", "by", "from", "to", "before", "after", "between", "during", "in"]}, "OP": "?"}, {"ENT_TYPE": {"in": ["DATE", "TIME"]}, "OP": "+"}],
[{"LOWER": {"in": terms1}, "OP": "?"}, {"LEMMA": {"in": terms2}, "OP": "?"}, {"LEMMA": {"in": terms3}, "OP": "+"}],
[{"LOWER": {"in": terms1}, "OP": "?"}, {"LEMMA": {"in": terms2}, "OP": "?"}, {"LEMMA": {"in": terms3}, "OP": "?"}, {"ENT_TYPE": {"in": ["DATE", "TIME"]}, "OP": "+"}],
[{"LOWER": {"in": terms1}, "OP": "?"}, {"LEMMA": {"in": terms2}, "OP": "?"}, {"LEMMA": {"in": terms3}, "OP": "?"}, {"LOWER": {"in": ["at", "on", "by", "from", "to", "before", "after", "between", "during", "in"]}, "OP": "?"}, {"ENT_TYPE": {"in": ["DATE", "TIME"]}, "OP": "+"}]
]
[docs]
self.matcher = SimpleEntityMatcher(nlp, label='Temporal', patterns=pattern)
[docs]
def __call__(self, doc):
"""
Args:
doc: spacy.tokens.doc.Doc, the processed document using nlp pipelines
"""
matches = list(re.finditer(self.datePattern, doc.text, re.VERBOSE))
newEnts = []
for match in matches:
startChar, endChar = match.span()
# Convert character offsets to token offsets
startToken = None
endToken = None
for token in doc:
if token.idx == startChar:
startToken = token.i
if token.idx + len(token.text) == endChar:
endToken = token.i
if startToken is not None and endToken is not None:
# hitText = doc.text[startChar:endChar]
ent = Span(doc, startToken, endToken + 1, label="Temporal")
newEnts.append(ent)
## Following is used to add a custom attribute to indicate Temporal
# parsed_date = dateparser.parse(hitText)
# if parsed_date: # Ensure the matched string is a valid date
# ent = Span(doc, startToken, endToken + 1, label="Temporal")
# ent._.date = parsed_date
# newEnts.append(ent)
# else:
# # Replace each ordinal in hitText with its numeric representation
# for ordinal, number in self.ordinalToNumber.items():
# hitText = hitText.replace(ordinal, number)
# # Remove the word "of" from hitText
# new_date = hitText.replace(" of ", " ")
# parsed_date = dateparser.parse(new_date)
# ent = Span(doc, startToken, endToken + 1, label="Temporal")
# ent._.date = parsed_date
# newEnts.append(ent)
# Combine the new entities with existing entities, ensuring no overlap
doc.ents = filter_spans(newEnts+list(doc.ents))
# Using SimpleEntityMatcher
doc = self.matcher(doc, replace=True)
return doc