mirror of
https://github.com/ilri/csv-metadata-quality.git
synced 2024-11-14 18:17:05 +01:00
Alan Orth
a21ffb0fa8
Faster and more modern code for Python 3 as a drop-in replacement. See: https://adrien.barbaresi.eu/blog/language-detection-langid-py-faster.html
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
# SPDX-License-Identifier: GPL-3.0-only
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import re
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import pandas as pd
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import py3langid as langid
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from colorama import Fore
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from pycountry import languages
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def correct_language(row, exclude):
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"""Analyze the text used in the title, abstract, and citation fields to pre-
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dict the language being used and compare it with the item's dc.language.iso
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field.
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Function prints an error if the language field does not match the detected
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language and returns the value in the language field if it does match.
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"""
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# Initialize some variables at global scope so that we can set them in the
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# loop scope below and still be able to access them afterwards.
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language = ""
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sample_strings = []
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title = None
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# Iterate over the labels of the current row's values. Before we transposed
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# the DataFrame these were the columns in the CSV, ie dc.title and dc.type.
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for label in row.axes[0]:
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# Skip fields with missing values
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if pd.isna(row[label]):
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continue
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# Check if current row has multiple language values (separated by "||")
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match = re.match(r"^.*?language.*$", label)
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if match is not None:
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# Skip fields with multiple language values
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if "||" in row[label]:
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return
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language = row[label]
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# Extract title if it is present (note that we don't allow excluding
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# the title here because it complicates things).
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match = re.match(r"^.*?title.*$", label)
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if match is not None:
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title = row[label]
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# Append title to sample strings
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sample_strings.append(row[label])
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# Extract abstract if it is present
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match = re.match(r"^.*?abstract.*$", label)
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if match is not None and label not in exclude:
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sample_strings.append(row[label])
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# Extract citation if it is present
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match = re.match(r"^.*?[cC]itation.*$", label)
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if match is not None and label not in exclude:
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sample_strings.append(row[label])
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# Make sure language is not blank and is valid ISO 639-1/639-3 before proceeding with language prediction
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if language != "":
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# Check language value like "es"
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if len(language) == 2:
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if not languages.get(alpha_2=language):
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return
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# Check language value like "spa"
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elif len(language) == 3:
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if not languages.get(alpha_3=language):
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return
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# Language value is something else like "Span", do not proceed
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else:
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return
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# Language is blank, do not proceed
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else:
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return
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# Concatenate all sample strings into one string
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sample_text = " ".join(sample_strings)
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# Restrict the langid detection space to reduce false positives
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langid.set_languages(
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["ar", "de", "en", "es", "fr", "hi", "it", "ja", "ko", "pt", "ru", "vi", "zh"]
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)
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langid_classification = langid.classify(sample_text)
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# langid returns an ISO 639-1 (alpha 2) representation of the detected language, but the current item's language field might be ISO 639-3 (alpha 3) so we should use a pycountry Language object to compare both represenations and give appropriate error messages that match the format used by in the input file.
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detected_language = languages.get(alpha_2=langid_classification[0])
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if len(language) == 2 and language != detected_language.alpha_2:
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print(
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f"{Fore.YELLOW}Possibly incorrect language {language} (detected {detected_language.alpha_2}): {Fore.RESET}{title}"
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)
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elif len(language) == 3 and language != detected_language.alpha_3:
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print(
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f"{Fore.YELLOW}Possibly incorrect language {language} (detected {detected_language.alpha_3}): {Fore.RESET}{title}"
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)
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else:
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return
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