Detectar the language of text
Language detection identifies el language de un texto sample usando carácter n-grams, detener palabras, y statistical models. Aprender común approaches.
- 1
Language detection methods: (1) Stop word analysis — each language has characteristic stop words (the, and, of for English; le, et, de for French). (2) Character n-grams — each language has characteristic letter sequences (sch for German, ll for Spanish). (3) Statistical models (Naive Bayes, neural networks) trained on labeled text. Libraries: franc, cld3, langdetect.
- 2 Contador de palabras →
Contar words and compare against stop word lists for different languages. If the text contains many English stop words (the, is, at), it is likely English. This is a simple but effective heuristic for common languages.
- 3 Frecuencia de caracteres →
Analyze character frequency distribution. English has high frequency of e, t, a, o, i, n. German has sch, ä, ö, ü. French has ç, accents. CJK languages have no word boundaries. Character distribution is a strong language signal, especially for short texts.
- 4 Contador de oraciones de texto →
Contar sentences to understand text structure. Some languages use different sentence-ending punctuation (。for CJK, ¿? for Spanish questions). Sentence structure and punctuation patterns are additional language signals.
Preguntas frecuentes
- How accurate is language detection?
- For long texts (>100 characters) in common languages, 95-99% accuracy. For short texts or similar languages (Spanish vs Portuguese, Norwegian vs Danish), accuracy drops. Mixed-language texts are problematic. Use a library (franc, cld3) for best results — they handle edge cases that simple heuristics miss.
- How do I detect language for CJK text?
- CJK languages (Chinese, Japanese, Korean) share some characters but have distinguishing features: Japanese has hiragana (ひらがな) and katakana (カタカナ), Korean has Hangul (한글), Chinese uses only Hanzi (汉字). Detect by the presence of these unique character sets. Use Unicode block analysis.