Temporal Expression Normalization for spaCy (TeNs)

Temporal Expressions Normalization spaCy (TeNs) is a powerful pipeline component for spaCy that seamlessly identifies and parses date entities in text. It leverages the Temporal Expressions Normalization Framework to recognize a wide variety of date formats using an extensive set of regular expressions (RegEx), ensuring robust and adaptable date extraction across diverse textual sources.

Unlike conventional solutions that primarily focus on well-structured date formats, TeNs excels in handling real-world text by identifying not only standard date representations but also abbreviated, informal, or even misspelled temporal expressions. This makes it particularly effective for processing noisy or unstructured data, such as historical records, user-generated content, and scanned documents with OCR inaccuracies.

Moreover, TeNs is designed to integrate seamlessly into existing NLP pipelines, allowing for enhanced temporal information processing in tasks such as event extraction, timeline construction, and knowledge graph population. By providing a flexible and accurate approach to temporal data normalization, it significantly improves the quality and reliability of date-related information extracted from text.

Table I. Types of temporal expressions which can be processed

Type of Temporal Expressions	Examples of Temporal Expressions*
dates	YMD: 1881-08-31; 1857 mai 10; etc. DMY: 09.11.1518; 1 noiembrie 1624; etc. MY: ianuarie 632; etc.
timespans	centuries: s:; sc; se.; sec; sec.; secol; secolele; secolul; sex. millenniums: mil; mil.; mileniul; mileniului; mileniile
years	77; 78; 1652; [1873]; aproximativ 1834; cca. 1420; etc.

* The values are mentioned in the reference language – Romanian language

Getting started

Prerequisites

• Python 3.9-3.12
• JRE 11-17
• spaCy >=3.8.7,<4.0.0
• py4j 0.10.9.9
• langdetect 1.0.9

Install

pip install temporal-normalization-spacy

Supported languages

• Romanian

Use with spaCy library

Importing Modules & Defining Constants

import subprocess

import spacy

from temporal_normalization.commons.print_utils import console

LANG = "ro"
MODEL = "ro_core_news_sm"
TEXT_RO = ("Sec al II-lea a.ch. a fost o perioadă de mari schimbări. "
           "În secolul XX, tehnologia a avansat semnificativ. "
           "Sec. 21 este adesea asociat cu globalizarea rapidă.")

Adding the Component to spaCy Pipeline

# Display a warning if the language of the text is not Romanian.
console.lang_warning(TEXT_RO, target_lang=LANG)

try:
    # Load the spaCy model if it has already been downloaded
    nlp = spacy.load(MODEL)
except OSError:
    console.warning(f'Started downloading {MODEL}...')
    # Download the Romanian model if it wasn't already downloaded
    subprocess.run(["python3", "-m", "spacy", "download", MODEL])
    # Load the spaCy model
    nlp = spacy.load(MODEL)

# Add "temporal_normalization" component to the spaCy pipeline
nlp.add_pipe("temporal_normalization", last=True)

Processing Text with the Pipeline

doc = nlp(TEXT_RO)

# Display NLP-specific linguistic annotations
console.tokens_table(doc)
print()

Accessing the Parsed Temporal Expressions

# Display information about the identified and normalized dates in the text.
for entity in doc.ents:
    time_series = entity._.time_series

    if isinstance(time_series, list):
        for ts in time_series:
            edges = ts.edges

            print("Start Edge:")
            print(edges.start.serialize("\t"))
            print()

            print("End Edge:")
            print(edges.end.serialize("\t"))
            print()

            print("Periods:")
            for period in ts.periods:
                print(period.serialize("\t"))
                print()
            print("---------------------")

Standalone usage

** Important Note: Even if you choose the standalone approach, the spaCy library will still be loaded on first run, and this process may take a few seconds/tens of seconds.

Importing Modules & Defining Constants

from pathlib import Path

from temporal_normalization import (
    close_conn,
    console,
    extract_temporal_expressions,
    start_conn,
    TemporalExpression,
)

LANG = "ro"
TEXT_RO = (
    "Sec al II-lea a.ch. a fost o perioadă de mari schimbări. "
    "În secolul XX, tehnologia a avansat semnificativ. "
    "Sec. 21 este adesea asociat cu globalizarea rapidă."
)

Parsing the Content

# Display a warning if the language of the text is not Romanian.
console.lang_warning(TEXT_RO, target_lang=LANG)

root_path = str(Path(__file__).resolve().parent.parent.parent)
java_process, gateway = start_conn(root_path)
expressions: list[TemporalExpression] = extract_temporal_expressions(gateway, TEXT_RO)
close_conn(java_process, gateway)

Accessing the Parsed Temporal Expressions

# Display information about the identified and normalized dates in the text.
for expression in expressions:
    for time_series in expression.time_series:
        edges = time_series.edges

        print("Start Edge:")
        print(edges.start.serialize("\t"))
        print()

        print("End Edge:")
        print(edges.end.serialize("\t"))
        print()

        print("Periods:")
        for period in time_series.periods:
            print(period.serialize("\t"))
            print()
        print("---------------------")

Result

First Sentence

Start Edge:
	Matched value: Sec al II-lea a.ch.
	Matched Type: century
	Normalized label: 2nd century BC
	DBpedia uri: https://dbpedia.org/resource/2nd_century_BC

End Edge:
	Matched value: Sec al II-lea a.ch.
	Matched Type: century
	Normalized label: 2nd century BC
	DBpedia uri: https://dbpedia.org/resource/2nd_century_BC

Periods:
	Matched value: Sec al II-lea a.ch.
	Matched Type: century
	Normalized label: 1st millennium BC
	DBpedia uri: https://dbpedia.org/resource/1st_millennium_BC

	Matched value: Sec al II-lea a.ch.
	Matched Type: century
	Normalized label: 2nd century BC
	DBpedia uri: https://dbpedia.org/resource/2nd_century_BC

Second Sentence

Start Edge:
	Matched value: secolul XX
	Matched Type: century
	Normalized label: 20th century
	DBpedia uri: https://dbpedia.org/resource/20th_century

End Edge:
	Matched value: secolul XX
	Matched Type: century
	Normalized label: 20th century
	DBpedia uri: https://dbpedia.org/resource/20th_century

Periods:
	Matched value: secolul XX
	Matched Type: century
	Normalized label: 2nd millennium
	DBpedia uri: https://dbpedia.org/resource/2nd_millennium

	Matched value: secolul XX
	Matched Type: century
	Normalized label: 20th century
	DBpedia uri: https://dbpedia.org/resource/20th_century

Third Sentence

Start Edge:
	Matched value: Sec. 21
	Matched Type: century
	Normalized label: 21st century
	DBpedia uri: https://dbpedia.org/resource/21st_century

End Edge:
	Matched value: Sec. 21
	Matched Type: century
	Normalized label: 21st century
	DBpedia uri: https://dbpedia.org/resource/21st_century

Periods:
	Matched value: Sec. 21
	Matched Type: century
	Normalized label: 3rd millennium
	DBpedia uri: https://dbpedia.org/resource/3rd_millennium

	Matched value: Sec. 21
	Matched Type: century
	Normalized label: 21st century
	DBpedia uri: https://dbpedia.org/resource/21st_century

Publications

ECAI 2021: The Power of Regular Expressions in Recognizing Dates and Epochs (2021)

@inproceedings{9515139,
  author={Dorobăț, Ilie Cristian and Posea, Vlad},
  booktitle={2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)}, 
  title={The Power of Regular Expressions in Recognizing Dates and Epochs}, 
  year={2021},
  pages={1-3},
  doi={10.1109/ECAI52376.2021.9515139}}