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จॻྨɿݴޠಉఆ !4 จࣈόΠάϥϜͷόοάʢCBHPGMFUUFSCJHSBNTʣ͕ڧྗ @btsmith #nlp ▪ Character n-gram frequencies for English Language Identification 28 e 12.6% t 9.1% a 8.0% o 7.6% i 6.9% n 6.9% s 6.3% h 6.2% … th 3.9% he 3.7% in 2.3% er 2.2% an 2.1% re 1.7% nd 1.6% on 1.4% … the 3.5% and 1.6% ing 1.1% her 0.8% hat 0.7% his 0.6% tha 0.6% ere 0.6% … From Cryptograms.org, derived from English documents at Project Gutenberg https://www.slideshare.net/LithiumTech/lightweight-natural-language-processing-nlp
จॻྨɿݴޠಉఆ !5 จࣈූ߸ԽํࣜಉఆʢFODPEJOHEFUFDUJPOʣʹόΠτόΠάϥϜͷόοά͕༗ޮ Figure 2: Byte-based method vs. character-based method – ISO-2022-{JP,KR} [ja,ko] – UTF-8 [universal] or characters (unigram models can use two or ters (bigrams, trigram parameter space is exp between the accuracy ing, computation and s tant as the size of the Asian charsets with ch 3.3 Algorithm Our first choice was N http://cs229.stanford.edu/proj2007/KimPark-AutomaticDetectionOfCharacterEncodingAndLanguages.pdf
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࣍ !16 ୈ̎ฤɹࣗવݴޠσʔλͷѻ͍ ‣ ୈ̒ষɹςΩετσʔλͷͨΊͷૉੑ ‣ ୈ̓ষɹࣄྫݚڀɿࣗવݴޠॲཧʹ͓͚Δૉੑ w จॻྨɿݴޠಉఆ w จॻྨɿτϐοΫྨ w จॻྨɿஶऀಛఆ w จ຺ʹຒΊࠐ·Εͨ୯ޠɿࢺλά͚ w จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ w จ຺ʹຒΊࠐ·Εͨ୯ޠͱݴޠֶతૉੑɿલஔࢺҙຯᐆດੑղফ w จ຺ʹຒΊࠐ·Εͨ୯ޠͷؒͷؔɿΞʔΫΛ୯Ґͱͨ͠ύʔδϯά