Tirsvad
7d8cc4522e
Compares a rule-based method, langdetect, langid and lingua on the corpus. langid with a pre-filter and a 0.6 threshold flags 0 of 300 English comments, 44/45 Danish and 41/45 Malay. The comparison script is kept next to the corpus. Refs #198 Task: MIL-013#2
121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
"""MIL-013 task 2 (ADR-0012): compare detection methods on the corpus.
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Needs langdetect, langid and lingua-language-detector installed (not part of the
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test requirements). Usage: python compare_detectors.py [-v]
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"""
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import csv
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import re
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import sys
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import time
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from collections import defaultdict
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from pathlib import Path
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CORPUS = Path(__file__).resolve().parent
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rows = []
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for name in ("english.tsv", "foreign.tsv"):
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with (CORPUS / name).open(encoding="utf-8", newline="") as handle:
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rows += [(r["lang"], r["text"]) for r in csv.DictReader(handle, delimiter="\t", quoting=csv.QUOTE_NONE)]
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# ---------- A: rule-based
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NON_ENGLISH_LETTERS = re.compile(r"[æøåäöüßéèêàâçñôîûœ]", re.I)
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STOP = {
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"en": "the a an of to and is in it that this for on with as are be by or if not from at which can will we you "
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"when then use used only each all any its into has have was were been do does done so but also than how what".split(),
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"da": "og er det en at i jeg til på for med af ikke den der som vi skal kan har de om men et fra hvis her kun "
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"efter før bliver være dette denne mappen filen listen brugeren serveren tjek hent gem slet læs opret".split(),
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"ms": "dan yang untuk dengan ini itu di dalam pada akan atau tidak dari ke kita jika boleh adalah mesti sama "
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"semak fail pelayan pengguna senarai kira baris guna supaya apabila sebelum selepas daripada".split(),
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"other": "der die das und ist nicht ein eine mit von zu den dem auf für wir och att som en är inte för med "
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"på av vi le la les des est une pour pas dans que qui nous sur om det jeg å ikke vi må".split(),
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}
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def rule_based(text: str) -> bool:
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if NON_ENGLISH_LETTERS.search(text):
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return True
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words = re.findall(r"[a-zA-ZæøåÆØÅ']+", text.lower())
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score = {k: sum(w in set(v) for w in words) for k, v in STOP.items()}
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foreign = max(score["da"], score["ms"], score["other"])
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return foreign >= 2 and foreign > score["en"]
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# ---------- B: langdetect
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def make_langdetect():
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from langdetect import DetectorFactory, detect_langs
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DetectorFactory.seed = 0
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def run(text: str, threshold: float = 0.8) -> bool:
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try:
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best = detect_langs(text)[0]
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except Exception:
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return False
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return best.lang != "en" and best.prob >= threshold
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return run
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# ---------- C: langid
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def make_langid():
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import langid
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langid.set_languages(["en", "da", "no", "sv", "de", "fr", "ms", "id", "nl"])
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def run(text: str, threshold: float = 0.0) -> bool:
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lang, _score = langid.classify(text)
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return lang != "en"
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return run
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# ---------- D: lingua
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def make_lingua():
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from lingua import Language, LanguageDetectorBuilder
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names = ["ENGLISH", "DANISH", "BOKMAL", "NYNORSK", "SWEDISH", "GERMAN", "FRENCH", "MALAY", "INDONESIAN", "DUTCH"]
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languages = [getattr(Language, n) for n in names if hasattr(Language, n)]
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print("lingua languages:", [l.name for l in languages], file=sys.stderr)
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detector = LanguageDetectorBuilder.from_languages(*languages).build()
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def run(text: str, threshold: float = 0.5) -> bool:
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values = detector.compute_language_confidence_values(text)
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if not values:
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return False
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top = values[0]
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return top.language != Language.ENGLISH and top.value >= threshold
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return run
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METHODS = {"rule": rule_based}
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for name, factory in (("langdetect", make_langdetect), ("langid", make_langid), ("lingua", make_lingua)):
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try:
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METHODS[name] = factory()
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except Exception as error: # a method that cannot be built is reported, not hidden
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print("cannot build", name, error, file=sys.stderr)
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def report(name: str, predict) -> None:
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started = time.perf_counter()
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flagged = defaultdict(lambda: [0, 0]) # lang -> [flagged, total]
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wrong = []
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for lang, text in rows:
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is_flagged = predict(text)
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flagged[lang][1] += 1
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if is_flagged:
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flagged[lang][0] += 1
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if (lang == "en") == is_flagged:
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wrong.append((lang, text))
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elapsed = time.perf_counter() - started
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foreign = [k for k in flagged if k != "en"]
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line = f"{name:12s} " + " ".join(f"{k}={flagged[k][0]}/{flagged[k][1]}" for k in sorted(foreign))
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print(f"{line} FALSE-POSITIVES en={flagged['en'][0]}/{flagged['en'][1]} {elapsed:.2f}s/{len(rows)}")
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if "-v" in sys.argv:
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for lang, text in wrong[:25]:
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print(" ", lang, "|", text[:80])
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for name, predict in METHODS.items():
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report(name, predict)
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