src.dackar.pipelines.TemporalEntity

Classes

Temporal

Temporal Entity Recognition class

Functions

find_temporal(nlp, name)

Module Contents

src.dackar.pipelines.TemporalEntity.find_temporal(nlp, name)

class src.dackar.pipelines.TemporalEntity.Temporal(nlp)

Bases: object

Temporal Entity Recognition class

How to use it:

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:

nlp.add_pipe('Temporal')
newDoc = nlp(doc.text)

name = 'Temporal'

ordinalToNumber

datePattern = Multiline-String

Show Value

"""
        # 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
        (?:
            \b(?:)\b
            \s+
            (?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]* # Month name
            (?:                         # Year is optional
                \s+
                \d{4}                   # Year
            )?
        )
        |
        (?:
            \b(?:)\b
            \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+
            \b(?:)\b
            (?:                         # Year is optional
                \s+
                \d{4}                   # Year
            )?
        )
        |
        (?:
            \d+
            (?:\-|\s+)?
            (?:)\b
        )
    """

matcher

asSpan = True

__call__(doc)

Parameters:

doc – spacy.tokens.doc.Doc, the processed document using nlp pipelines