src.dackar.pipelines.TemporalEntity

Classes

Temporal

How to use it:

Functions

find_temporal(nlp, name)

Module Contents

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

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

Bases: object

How to use it:

from TemporalEnity import Temporal
nlp = spacy.load("en_core_web_sm")
pmatcher = Temporal(nlp)
doc = nlp("The shaft deflection is causing the safety cage to rattle. Pumps not experiencing enough flow for the pumps to keep the check valves open during test. Pump not experiencing enough flow during test. Shaft made noise. Vibration seems like it is coming from the shaft.")
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