20190407 第7章 事例研究:自然言語処理における素性

本郷言語処理勉強会 #3 で用いた資料。
https://holy.connpass.com/event/122295/
「ゴールドバーグ, 『自然言語処理のための深層学習』, 共立出版, 2019」輪読会。

Transcript

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4. 
   จॻ෼ྨɿݴޠಉఆ !4  จࣈόΠάϥϜͷόοάʢCBH
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   MFUUFSCJHSBNTʣ͕ڧྗ @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
5. 
   จॻ෼ྨɿݴޠಉఆ !5  จࣈූ߸ԽํࣜಉఆʢFODPEJOH
   EFUFDUJPOʣʹ͸όΠτόΠάϥϜͷόοά͕༗ޮ 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
6. 
   จॻ෼ྨɿτϐοΫ෼ྨ !6  จষ
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 ʢޠॱ͸͋·ΓҙຯΛ࣋ͨͳ͍ʣ
   ‣ ୯ޠͷόοά
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   w ϓϥεͰɺϨϯϚԽɺ୯ޠຒΊࠐΈϕΫτϧɺ5'*%'ॏΈ෇͚ͳͲ΋ޮՌ͋Γ https://aylien.com/text-api/text-classification/
7. 
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    จষͷελΠϧʹؔ͢Δಛ௃͕༗༻
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8. 
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   ‣ Ұจ͋ͨΓͷฏۉ୯ޠ਺
   ‣ Ұจͷ௕͞ͷ෼ࢄ
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   ‣ Ұจ͋ͨΓͷΧϯϚɺηϛίϩϯɺίϩϯͷฏۉ਺
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9. 
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   ‣ ߏ଄໰୊ɺ·ͨ͸྆ଆ̎୯ޠͷ૭ʹ͓͚Δ඼ࢺλά෼ྨͷλεΫʹۙࣅ
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10. 
    จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ !10 タスク 説明: 固有表現抽出 • テキスト 中から人名や組織名 ような固有表現 (NE) を抽出する • 固有表現 定義 ほしい情報に基づいて柔軟に定義する • 情報抽出や検索エンジン インデキシングなどで有用 https://explosion.ai/demos/displacy-ent 5  https://speakerdeck.com/himkt/neural-named-entity-recognition
11. 
    จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ !11  ܥྻϥϕϦϯά໰୊
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12. 
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13. 
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    ‣ Ή͠ΖώϡʔϦεςΟοΫʢࠨଆͷ࠷ॳͷಈࢺˍӈଆͷ࠷ॳͷ໊ࢺʣ͕༗ޮ
    w લஔࢺͷࢧ഑ཁૉ
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