{"id":2616,"date":"2024-02-07T14:35:46","date_gmt":"2024-02-07T14:35:46","guid":{"rendered":"https:\/\/artiwise.com\/tr\/?p=2616"},"modified":"2026-02-27T08:22:14","modified_gmt":"2026-02-27T08:22:14","slug":"dogal-dil-isleme-nlp-nedir","status":"publish","type":"post","link":"https:\/\/artiwise.com\/tr\/2024\/02\/07\/dogal-dil-isleme-nlp-nedir\/","title":{"rendered":"Do\u011fal Dil \u0130\u015fleme (NLP) Nedir?"},"content":{"rendered":"\t\t
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NLP (Natural Language Processing), T\u00fcrk\u00e7e ismiyle Do\u011fal Dil \u0130\u015fleme (DD\u0130)<\/b>, yapay zekan\u0131n alt\u0131nda yer alan ve amac\u0131 insanlar\u0131n \u00fcretti\u011fi do\u011fal dilin i\u015flenebilmesi, anla\u015f\u0131labilmesi, analiz edilerek \u00e7\u0131kt\u0131lara d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilmesini sa\u011flayan bir metin analiti\u011fi ve ara\u015ft\u0131rma alan\u0131d\u0131r.<\/p>

G\u00fcnl\u00fck hayatta s\u0131k\u00e7a ba\u015fvurdu\u011fumuz ak\u0131ll\u0131 asistanlar, dil \u00e7eviri uygulamalar\u0131, \u00e7a\u011fr\u0131 merkezlerinde kullan\u0131lan IVR (sesli yan\u0131t) uygulamalar\u0131 arkas\u0131nda Do\u011fal Dil \u0130\u015fleme teknolojilerini bar\u0131nd\u0131rmaktad\u0131r.<\/p>

Bunlarla beraber en \u00f6nemli kullan\u0131m alanlar\u0131n\u0131, M\u00fc\u015fteri Memnuniyeti Analizi, <\/b>Chatbot Konu\u015fma Analizi<\/b>, Sosyal Medya Analizi, Anlamsal Arama<\/b> gibi \u00e7al\u0131\u015fmalar olu\u015fturmaktad\u0131r.\u00a0<\/p>

\u00a0<\/p>

Dilbilimin Alanlar\u0131<\/strong><\/h4>

Bir do\u011fal dile ait t\u00fcm \u00e7al\u0131\u015fmalar dil biliminde 6 ba\u015fl\u0131kta de\u011ferlendirilmektedir. Bu ba\u015fl\u0131klar \u015fu \u015fekilde s\u0131ralanabilir;<\/b><\/p>

  1. Fonetik (Phonetics):<\/b> \u0130nsanlar\u0131n dili konu\u015furken ne gibi sesler kulland\u0131klar\u0131, bu seslerde ne gibi \u00f6r\u00fcnt\u00fcler bulundu\u011fu ile ilgilenen alan<\/li>
  2. Fonoloji (Phonology):<\/b> Dili meydana getiren ses birliktelikleri ile ilgilenen alan<\/li>
  3. Morfoloji (Morphology): <\/b>\u00a0Dildeki kelimelerin yap\u0131lar\u0131n\u0131 inceleyen, anlam ifade eden en k\u00fc\u00e7\u00fck k\u0131sm\u0131ndan (k\u00f6k\u00fcnden) itibaren hangi eklerle ve hangi durumlarda nas\u0131l anlamlar kazand\u0131\u011f\u0131 ile ilgilenen alan<\/li>
  4. S\u00f6z Dizimi (Syntax): <\/b>C\u00fcmle yap\u0131lar\u0131 ile, s\u00f6zc\u00fck gruplar\u0131 veya c\u00fcmleler olu\u015furken kelimelerin hangi dizilimlerle bir araya gelebilece\u011fi ile ilgilenen alan<\/li>
  5. Anlam Bilim (Semantics):<\/b> C\u00fcmleler, c\u00fcmle \u00f6geleri, kelimeler, hatta kelime k\u00f6kleri\/eklerinin ve bunlar\u0131n bir araya geli\u015f durumlar\u0131n\u0131n anlam\u0131 nas\u0131l etkiledi\u011fi ile ilgilenen alan<\/li>
  6. Edim Bilim (Pragmatics): <\/b>\u00a0Niyetin ve ba\u011flam\u0131n ifadenin anlam\u0131 \u00fczerindeki etkisi ve ifadelerin dilin kullan\u0131m\u0131 s\u0131ras\u0131nda kazand\u0131\u011f\u0131 anlamlar ile ilgilenen alan<\/li><\/ol>

    \u00a0<\/p>

    Do\u011fal Dil \u0130\u015fleme (NLP) Ad\u0131mlar\u0131<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Do\u011fal\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t

    Do\u011fal dil i\u015fleme nedir sorusunun cevab\u0131n\u0131 \u00f6\u011frendikten sonra do\u011fal dil i\u015fleme ad\u0131mlar\u0131ndan da bahsetmek \u00f6nemli olacakt\u0131r. Do\u011fal dil i\u015flenirken g\u00fcnl\u00fck kullan\u0131mda olan bir ifade veya metnin sistematik kurall\u0131 bir metne \u00e7evrilmesi i\u00e7in pek \u00e7ok a\u015famadan ge\u00e7irilmesi gerekmektedir.<\/b><\/p>

    \u00a0<\/p>

    Konu\u015fmay\u0131 Yaz\u0131ya D\u00f6n\u00fc\u015ft\u00fcrme (Speech-to-Text)<\/strong><\/h4>

    Ses kayd\u0131 olarak elde edilen bir ifadenin yaz\u0131l\u0131 bi\u00e7ime d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesidir.<\/b> Yaz\u0131ya d\u00f6n\u00fc\u015ft\u00fcrme algoritmalar\u0131nda NLP alan\u0131n\u0131n deste\u011fi sinyal i\u015fleme algoritmalar\u0131n\u0131n tespit etti\u011fi sesleri dil modeli \u00fczerinde i\u015fleyerek en seslerle olu\u015fturulabilecek en olas\u0131 kelime ve c\u00fcmleleri olu\u015fturmakt\u0131r. Konu\u015fmay\u0131 yaz\u0131ya d\u00f6n\u00fc\u015ft\u00fcrme NLP kullan\u0131m\u0131na \u015fu tarz durumlar \u00f6rnek olarak verilebilir:<\/p>

    • Yaz\u0131ld\u0131\u011f\u0131 \u015fekilde okunmayan dillerde <\/b>ayn\u0131 seslerle telaffuz edilebilir kelimeler i\u00e7inden c\u00fcmle ak\u0131\u015f\u0131na g\u00f6re en uygun anlaml\u0131 kelimenin se\u00e7ilmesi,<\/li>
    • Yaz\u0131ld\u0131\u011f\u0131 \u015fekilde okunan dillerde<\/b> alg\u0131lanan sesler belirli bir kelimeye tekab\u00fcl etmiyorsa veya bulunan kelime metnin anlamsal uyumunu bozuyorsa benzerlik algoritmalar\u0131ndan da destek al\u0131narak en uygun olas\u0131 kelimenin se\u00e7ilmesi,<\/li>
    • Konu\u015fman\u0131n kalitesinin kelimelerin \u00e7\u0131k\u0131\u015flar\u0131n\u0131 sinyal i\u015fleme algoritmalar\u0131yla ay\u0131rt etmeye yetmedi\u011fi durumlarda<\/b>, harfler dizisinin en uygun noktalardan b\u00f6l\u00fcnerek s\u0131ral\u0131 kelimeler haline getirilmesi.<\/li><\/ul>

      \u00a0<\/p>

      Dil Tespiti (Language Detection)<\/strong><\/h4>

      Bir metnin hangi dilde yaz\u0131lm\u0131\u015f oldu\u011funun otomatik tespit edilmesidir. <\/b>Bu i\u015flemin yap\u0131labilmesi i\u00e7in yayg\u0131n kullan\u0131lan \u00e7e\u015fitli y\u00f6ntemlerden baz\u0131lar\u0131 alttaki gibidir:<\/p>

      • Dil biliminin \u00e7\u0131kar\u0131msad\u0131\u011f\u0131 dil kurallar\u0131 kullan\u0131larak metnin hangi dil oldu\u011funun kural tabanl\u0131 bir sistemle belirlenmesi<\/li>
      • Bir dile ait \u00e7ok say\u0131da veriden olu\u015fan bir veritaban\u0131nda ngram analizi yap\u0131larak test edilecek veride ge\u00e7en kelime ve kelime gruplar\u0131n\u0131n bu dilde kullan\u0131lanlarla uyumunun \u00f6l\u00e7\u00fclerek skorlanmas\u0131<\/li>
      • Yapay zeka algoritmalar\u0131 yard\u0131m\u0131yla her dil bir s\u0131n\u0131f olacak \u015fekilde e\u011fitilen modellerin kullan\u0131m\u0131<\/li><\/ul>

        Ngram analizi<\/b>, bir metin i\u00e7inde art arda gelen kelimeler veya harfer dizisinin s\u0131kl\u0131klar\u0131n\u0131 dikkate alarak genellikle metin i\u00e7inde hangi \u00f6r\u00fcnt\u00fclerin s\u0131kl\u0131kla ge\u00e7ti\u011fini belirlemek i\u00e7in yap\u0131lan analizlerde kullan\u0131l\u0131r.\u00a0<\/p>

        Ngramlar ayn\u0131 zamanda kelime dizilimlerinin \u00f6nemli oldu\u011fu baz\u0131 NLP g\u00f6revlerinde terim olarak kelimeler yerine kullan\u0131l\u0131r. N-gram kelimesindeki N kelime say\u0131s\u0131n\u0131 ifade eder. Mesela 1gram analizinde \u2018bula\u015f\u0131k\u2019 ve \u2018makine\u2019 kelimeleri ayr\u0131 ayr\u0131 dikkate al\u0131n\u0131rken 2gram analizinde \u2018bula\u015f\u0131k makinesi\u2019 ikilisinin ayr\u0131 bir anlam\u0131 ve kullan\u0131m alan\u0131 oldu\u011fu bilgi olarak e\u011fitilecek modele otomatik olarak aktar\u0131lm\u0131\u015f olur.\u00a0<\/p>

        Kullan\u0131m alan\u0131na bir \u00f6rnek verecek olursak<\/b> da 1gramlarla sadece terimin ge\u00e7ip ge\u00e7medi\u011fini dikkate alan bir duygu modeli e\u011fitildi\u011finde \u2018k\u00f6t\u00fc de\u011fil\u2019 ifadesi olumsuz olarak s\u0131n\u0131fland\u0131r\u0131lacakken 2gramlar bu e\u011fitimde terim olarak kullan\u0131lsa bu ifadenin b\u00fct\u00fcn olarak olumlu anlam\u0131 oldu\u011fu rahat\u00e7a model taraf\u0131ndan anla\u015f\u0131labilecektir.<\/p>

        \u00a0<\/p>

        Karakter D\u00fczeltme (Declassification)<\/strong><\/h4>

        T\u00fcrk\u00e7e karakter kullan\u0131lmadan yaz\u0131lm\u0131\u015f ifadelerdeki bu karakterlerin do\u011fru T\u00fcrk\u00e7e versiyonlar\u0131 ile de\u011fi\u015ftirilmesidir. Bu i\u015flem yap\u0131l\u0131rken T\u00fcrk\u00e7e\u2019nin yap\u0131sal \u00f6zelliklerinden faydalan\u0131lmaktad\u0131r.\u00a0<\/b><\/p>

        Dilimizde bulunan en temelleri k\u00fc\u00e7\u00fck \u00fcnl\u00fc uyumu, benze\u015fme vb. Olan yap\u0131sal kurallara uyum kontrol edilerek karakterlerde d\u00fczenleme yap\u0131lmas\u0131 en genel y\u00f6ntemdir. Bu kurallara ek olarak metnin ak\u0131\u015f\u0131n\u0131n da dikkate al\u0131nmas\u0131yla yaz\u0131m d\u00fczeltme gibi daha genel kullan\u0131ml\u0131 algoritmalar ortaya \u00e7\u0131kmaktad\u0131r.<\/p>

        \u00a0<\/p>

        Yaz\u0131m D\u00fczeltme (Spelling Correction)<\/strong><\/h4>

        Yaz\u0131m hatalar\u0131n\u0131n otomatik olarak tespit edilip d\u00fczeltilmesidir.<\/b> Bu d\u00fczenlemelerde genellikle benzerlik algoritmalar\u0131 ve dil modelleri kullan\u0131lmaktad\u0131r.<\/p>

        Dil modelleri (Language Models)<\/b> bir dilin yapay bir g\u00f6rev ile derin \u00f6\u011frenme methodlar\u0131yla e\u011fitilerek dil i\u00e7indeki kelime, kelime grubu ve c\u00fcmlelerin ak\u0131\u015f\u0131n\u0131n makineye \u00f6\u011fretilmesiyle olu\u015fan haz\u0131r modellerdir. Bu modeller genellikle bir sonraki kelimenin veya bir sonraki c\u00fcmlenin tahmin edilmesi veya c\u00fcmle i\u00e7inde maskelenmi\u015f verilerin tahmin edilmesi gibi g\u00f6revler ile e\u011fitilir.\u00a0<\/p>

        Bu g\u00f6revlerle yap\u0131lan e\u011fitimlerde sistem g\u00f6revi ba\u015far\u0131yla tamamlamak i\u00e7in t\u00fcm kelime ve c\u00fcmleler aras\u0131ndaki ba\u011flant\u0131lar\u0131 kendince anlam\u0131\u015f olmal\u0131d\u0131r. Bu yap\u0131 olu\u015fturulduktan sonra pek \u00e7ok NLP g\u00f6revinde yard\u0131mc\u0131 olarak rol alabilir.<\/p>

        Yaz\u0131m d\u00fczeltme algoritmalar\u0131nda da<\/b> dil modelleri o zamana kadar gelen kelimelere g\u00f6re bir sonraki muhtemel kelime i\u00e7in \u00f6neriler olu\u015fturabilir. Ve bu \u00f6neriler i\u00e7inden benzerlik algoritmas\u0131yla hatal\u0131 yaz\u0131ma en benzer olan \u00f6neri kullan\u0131labilir.<\/p>

        \u00a0<\/p>

        C\u00fcmle B\u00f6l\u00fcmleme (Sentence Segmentation)<\/strong><\/h4>

        Paragraf veya daha b\u00fcy\u00fck bir b\u00fct\u00fcn olarak bulunan metni c\u00fcmlelere veya kullan\u0131m alan\u0131na g\u00f6re fikir belirten daha k\u00fc\u00e7\u00fck b\u00f6lgelere (opinion unit)<\/b> ay\u0131rma i\u015fidir.<\/p>

        Bu konuda yap\u0131lan \u00e7al\u0131\u015fmalar ba\u015flang\u0131\u00e7ta kural tabanl\u0131 olarak, noktalama ve b\u00fcy\u00fck harf kullan\u0131m\u0131, s\u00f6z dizimi yap\u0131s\u0131na uyum gibi analizlerle \u00e7\u00f6z\u00fcmler \u00fcretmi\u015ftir. Fakat bu y\u00f6ntemler \u00f6zellikle g\u00fcn\u00fcm\u00fczde git gide artan kurals\u0131z sosyal medya, m\u00fc\u015fteri yorumu gibi verilerde y\u00fcksek ba\u015far\u0131 sa\u011flayamam\u0131\u015ft\u0131r.\u00a0<\/p>

        Bu konuda yap\u0131lan daha g\u00fcncel \u00e7al\u0131\u015fmalar daha \u00e7ok g\u00fcd\u00fcml\u00fc makine \u00f6\u011frenmesi y\u00f6ntemleri <\/b>ve dil modelleri<\/b> ile c\u00fcmleyi anlamsal olarak b\u00f6lmeye \u00f6nem veren derin \u00f6\u011frenme modelleri ile yap\u0131lmaktad\u0131r.<\/p>

        \u00a0<\/p>

        K\u00fc\u00e7\u00fck Harfe D\u00f6n\u00fc\u015ft\u00fcrme (Lowercase Converter)<\/strong><\/h4>

        B\u00fcy\u00fck harf kullan\u0131m\u0131n\u0131n \u00f6nemli olmad\u0131\u011f\u0131 durumlarda kelimelerin ortak olmas\u0131n\u0131 daha rahat yakalayabilmek i\u00e7in t\u00fcm harfleri k\u00fc\u00e7\u00fck harfe \u00e7evirir. En basit i\u015flemlerden olan bu ad\u0131mda bile dil parametresine dikkat edilmezse beklenmedik hatalar \u00e7\u0131kabilir.<\/p>

        <p> \u00d6r: Irmak -> \u0130ngilizce k\u00fc\u00e7\u00fck harfe \u00e7evirme -> irmak<\/b><\/p>

        -> T\u00fcrk\u00e7e k\u00fc\u00e7\u00fck harfe \u00e7evirme -> \u0131rmak<\/b><\/p>

        \u00a0<\/p>

        Terimlere Ay\u0131rma (Tokenization)<\/strong><\/h4>

        Metni kullan\u0131m amac\u0131na g\u00f6re i\u015flenebilir birimlere b\u00f6ler. <\/b>Bu b\u00f6l\u00fcmler bazen kelimeler, bazen c\u00fcmleler bazen de paragraflar veya tam ters y\u00f6nde heceler vs olabilir. Kullan\u0131m alanlar\u0131n\u0131n \u00e7o\u011funda anlam kelimelerin kullan\u0131m\u0131 ile belirlendi\u011fi i\u00e7in kelimeler terim olarak kullan\u0131lmaktad\u0131r.\u00a0<\/p>

        Kelimelere ay\u0131rma i\u015flemi yap\u0131l\u0131rken yine ilgili g\u00f6reve g\u00f6re birle\u015fik kelimelerin ayr\u0131lmas\u0131, alfanumerik olmayan karakterlerin dahil edilmesi gibi durumlarda g\u00f6reve \u00f6zel karar verilmesi gerekebilir.<\/p>

        \u00a0<\/p>

        Gereksiz Kelime Ay\u0131klama (Stopwords Removal)<\/strong><\/h4>

        Yap\u0131lacak analizlerde bir katk\u0131 sa\u011flamayan ve \u00e7ok s\u0131k ge\u00e7ti\u011fi i\u00e7in analizlerde \u00f6ne \u00e7\u0131kan kelimelerin ay\u0131klanmas\u0131 i\u015flemidir. (\u00f6r: ve, ama, bir, \u015fey\u2026)<\/b><\/p>

        Bu kelimelerin ay\u0131klanmas\u0131 \u00f6zellikle veri i\u00e7inde kelime bazl\u0131 bilgi \u00e7\u0131kar\u0131lmas\u0131 gerekti\u011fi durumlarda \u00f6nemlidir.\u00a0<\/p>

        Gereksiz kelimelerin ay\u0131klanmadan analizlere dahil edilmesi durumunda Ngram analizlerinde \u00f6zellikle gereksiz kelimelerin araya gelmesinden dolay\u0131 Ngramlar\u0131n s\u0131kl\u0131klar\u0131 do\u011fru bir \u015fekilde \u00f6l\u00e7\u00fclemeyebilir. Veya kelimelerin sadece varl\u0131klar\u0131 ile \u00f6\u011frenen modellerde konu ile ilgisi olmayan gereksiz kelimelerin metne eklenmesi atanacak s\u0131n\u0131f\u0131 beklenmedik \u015fekilde de\u011fi\u015ftirebilir.<\/p>

        \u00a0<\/p>

        \u0130stenmeyen \u00d6r\u00fcnt\u00fc Ay\u0131klama (Unwanted Pattern)<\/strong><\/h4>

        Metnin kayna\u011f\u0131na veya i\u00e7eri\u011fine ba\u011fl\u0131 olarak ortaya \u00e7\u0131k\u0131p metinlere anlamsal katk\u0131 sa\u011flamayan \u00f6r\u00fcnt\u00fclerin ay\u0131klanmas\u0131 i\u015flemidir.<\/b> (web sitesi linkleri, twitter etiketleri, hex karakterler vb.)<\/p>

        \u00d6zellikle internet sitelerinden otomatik olarak \u00e7ekilen verilerde istenmeyen karakterler s\u0131kl\u0131kla kar\u015f\u0131la\u015f\u0131lan bir durumdur. Bu verilerin ay\u0131klanmas\u0131 s\u00fcrecinde genellikle belirli \u00f6r\u00fcnt\u00fcleri yakalayan kurallar olu\u015fturmakla beraber terimlerin s\u0131kl\u0131klar\u0131 ve anlamasal analizleri yap\u0131larak bu \u00f6r\u00fcnt\u00fclerin tespitinin de otomatikle\u015ftirildi\u011fi algoritmalar kullan\u0131mdad\u0131r.<\/p>

        \u00a0<\/p>

        Normalizasyon (Text Normalization)<\/strong><\/h4>

        Metin i\u00e7inde ayn\u0131 anlam\u0131 payla\u015fan fakat c\u00fcmle i\u00e7indeki durumuna ba\u011fl\u0131 olarak farkl\u0131 \u00e7ekim ve yap\u0131m eki alm\u0131\u015f kelimelerin kullan\u0131m alan\u0131na g\u00f6re ortakla\u015ft\u0131r\u0131lmas\u0131n\u0131 sa\u011flayan ad\u0131mlard\u0131r.\u00a0<\/p>

        Burada verilerin ortakla\u015ft\u0131r\u0131lmas\u0131, veri seyrekli\u011fi (data sparsity) <\/b>nedeniyle algoritmalar\u0131n veriyi yeterince gruplay\u0131p anlamland\u0131ramad\u0131\u011f\u0131 durumlarda uyguland\u0131\u011f\u0131nda olduk\u00e7a faydal\u0131 olmaktad\u0131r.\u00a0<\/p>

        E\u011fer kelimelerin c\u00fcmle i\u00e7inde kullan\u0131m\u0131 ek bir bilgi kayna\u011f\u0131 de\u011filse normalizasyon ad\u0131mlar\u0131n\u0131n uygulanmas\u0131 mevcut bir problem olmasa bile veri kalitesini artt\u0131rarak ilgili g\u00f6revdeki ba\u015far\u0131y\u0131 da artt\u0131racakt\u0131r.<\/p>

        G\u00f6vdeleme (Lemmatization)<\/b> kelimeyi \u00e7ekim eklerinden ay\u0131klayarak en geni\u015f k\u00f6k\u00fcne indirger. Yap\u0131m eklerini ay\u0131klamayarak ayn\u0131 k\u00f6kten de olsa anlamsal farkl\u0131l\u0131klar\u0131 olan kelimelerin ay\u0131rt edilebilirliklerini korumaktad\u0131r.<\/p>

        K\u00f6ke \u0130ndirgeme (Stemming)<\/b> ise kelimeyi \u00e7ekim ve yap\u0131m eklerinden tamamen ay\u0131klayarak ayn\u0131 anlam\u0131n en k\u00fc\u00e7\u00fck k\u00f6k\u00fcne indirger.<\/p>

        \u00a0<\/p>

        C\u00fcmle \u00d6gelerini Belirleme (Part of Speech Tagging)<\/strong><\/h4>

        C\u00fcmle i\u00e7indeki kelime veya kelime gruplar\u0131n\u0131n c\u00fcmlenin hangi \u00f6\u011fesine hangi g\u00f6revle ba\u011fl\u0131 oldu\u011fu bilgisi ile etiketlenmesidir. <\/b>Bu i\u015flem i\u00e7in dil i\u00e7indeki s\u00f6z dizimi kurallar\u0131 kullan\u0131labildi\u011fi dil modelleri veya kelime vekt\u00f6rlerinin yard\u0131m\u0131yla e\u011fitilen g\u00fcd\u00fcml\u00fc \u00f6\u011frenme algoritmalar\u0131 da s\u0131kl\u0131kla bu i\u015flem i\u00e7in kullan\u0131lmaktad\u0131r.<\/p>

        \u00a0<\/p>

        Morfolojik Analiz (Morphological Analysis)<\/strong><\/h4>

        Kelimeleri tipine g\u00f6re (isim, fiil, s\u0131fat vb) s\u0131n\u0131fland\u0131rarak <\/b>c\u00fcmledeki olas\u0131 kullan\u0131m g\u00f6revinin tespitine yard\u0131mc\u0131 olur. Bu bilgi \u00f6zellikle varl\u0131k tan\u0131ma gibi kelimenin tipi ile yak\u0131ndan ilgili olan tasklarda \u00e7ok \u00f6nemlidir.<\/p>

        Bu analiz i\u00e7in mevcut pek \u00e7ok kural tabanl\u0131 sistem vard\u0131r. Bu sistemler dil \u00fczerindeki s\u00f6z dizimi kurallar\u0131na ek olarak dile ait s\u00f6zl\u00fcklerden de faydalanarak kelimeleri grupland\u0131rabilmektedir.\u00a0<\/p>

        Bu sistemlerin zay\u0131f yan\u0131 ise kural tabanl\u0131 olarak<\/b> olas\u0131 anlamlardan hangisinin metne uygun oldu\u011funun \u00e7\u00f6z\u00fcmlenememesidir. Bu \u00e7\u00f6z\u00fcmlemenin yap\u0131labilmesi i\u00e7in genellikle morfolojik analiz algoritmalar\u0131 kelime anlam \u00e7\u00f6z\u00fcmlemesi algoritmalar\u0131 ile beraber \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r.<\/p>

        \u00a0<\/p>

        Kelime Anlam \u00c7\u00f6z\u00fcmlemesi (Word Sense Disambiguation)<\/strong><\/h4>

        Metinde kullan\u0131lan kelimelerin olas\u0131 anlamlar\u0131 aras\u0131nda do\u011fru anlam\u0131n se\u00e7ilmesi i\u015flemidir. <\/b>Bu i\u015flemin sa\u011fl\u0131kl\u0131 i\u015fletilebilmesi i\u00e7in kullan\u0131lacak algoritmada sa\u011flam bir dil modeli bulunmas\u0131 \u00f6nemlidir.\u00a0<\/p>

        Alternatif olarak s\u00f6z dizimleri ile de kelime tipine g\u00f6re se\u00e7im yapan algoritmalar bulunmakla beraber genel ba\u015far\u0131lar\u0131 dil modeli kullanan sistemlere g\u00f6re zay\u0131f kalmaktad\u0131r. \u00d6rnek: Y\u00fcz verdik tepemize \u00e7\u0131kt\u0131lar. Numaram be\u015f y\u00fcz on. Sporcuyu daha h\u0131zl\u0131 y\u00fczs\u00fcn diye y\u00fcreklendirmeye \u00e7al\u0131\u015ft\u0131lar.<\/p>

        Mesela yukar\u0131daki metinlerde y\u00fcz kelimesi farkl\u0131 anlamlarda kullan\u0131lm\u0131\u015ft\u0131r.<\/b> Bu anlamlar\u0131n birbirinden ayr\u0131lmas\u0131 kelime anlam \u00e7\u00f6z\u00fcmlemesinin konusudur.<\/p>

        \u00a0<\/p>

        \u00d6zde\u015fik \u00c7\u00f6z\u00fcmleme (Coreference Resolution)<\/strong><\/h4>

        Bir metinde ayn\u0131 varl\u0131\u011fa g\u00f6nderme yapan t\u00fcm ifadelerin belirlenmesi i\u015flemidir.<\/b><\/p>

        \u00d6rnek: Ahmet Kerim\u2019den kap\u0131y\u0131 a\u00e7mas\u0131n\u0131 istedi. O da aya\u011fa kalk\u0131p kap\u0131y\u0131 a\u00e7t\u0131.<\/p>

        Mesela yukar\u0131daki metinde \u201cO\u201d zamiri ile belirtilen ki\u015finin Ahmet mi Kerim mi oldu\u011fu bilgisinin \u00e7\u0131kar\u0131lmas\u0131 \u00f6zde\u015flik \u00e7\u00f6z\u00fcmlemenin konusudur.<\/p>

        \u00a0<\/p>

        Do\u011fal Dil \u0130\u015fleme Kullan\u0131m Alanlar\u0131<\/strong><\/h4>

        Do\u011fal dil i\u015fleme nedir? Do\u011fal dil i\u015fleme ad\u0131mlar\u0131 nelerdir?<\/b> sorular\u0131n\u0131n cevaplar\u0131ndan sonra s\u0131ra do\u011fal dil i\u015fleme kullan\u0131m alanlar\u0131nda. Do\u011fal dil i\u015fleme \u00e7al\u0131\u015fma hayat\u0131nda ve g\u00fcnl\u00fck hayatta hepimizi ilgilendirebilen pek \u00e7ok aktif kullan\u0131m alan\u0131na sahiptir.<\/p>

        \u00a0<\/p>

        S\u0131n\u0131fland\u0131rma (Classification)<\/strong><\/h5>

        Metinlerin belirli konulara g\u00f6re gruplanmas\u0131 i\u00e7in yap\u0131lan \u00e7al\u0131\u015fmalar\u0131n tamamd\u0131r. <\/b>G\u00fcd\u00fcml\u00fc (supervised) s\u0131n\u0131fland\u0131rma y\u00f6ntemlerinde gruplar \u00f6nceden belirlenir ve her gruba ait belirli say\u0131da veri k\u00fcmesi elle i\u015faretlenir, ard\u0131ndan bu e\u011fitim verisi ile model e\u011fitilir.\u00a0<\/p>

        G\u00fcd\u00fcms\u00fcz (unsupervised)<\/b> y\u00f6ntemlerde is istenen grup say\u0131s\u0131 verilerek ilgili algoritman\u0131n verileri kendine \u00e7\u0131kar\u0131mlar\u0131na g\u00f6re en uygun gruplara ay\u0131rmas\u0131 beklenir. S\u0131n\u0131fland\u0131rma \u00e7al\u0131\u015fmalar\u0131 pek \u00e7ok alana uygulanabilir oldu\u011fu i\u00e7in NLP ve di\u011fer veri bilimi alanlar\u0131nda en s\u0131k kullan\u0131lan \u00e7al\u0131\u015fmad\u0131r.<\/p>

        \u00a0<\/p>

        Duygu Analizi (Sentiment Analysis)<\/strong><\/h4>

        Metinlerin ifade ettikleri duyguya g\u00f6re s\u0131n\u0131fland\u0131r\u0131lmas\u0131 i\u015flemidir. <\/b>Duygular pozitif, negatif, n\u00f6tr gibi 3 gruba ayr\u0131labilece\u011fi gibi, kullan\u0131m alan\u0131na g\u00f6re sevin\u00e7, \u00fcz\u00fcnt\u00fc, \u00f6fke, hayal k\u0131r\u0131kl\u0131\u011f\u0131 vb.gibi alternatif gruplara da ayr\u0131labilir.\u00a0<\/p>

        Dil modelinin dili yeterinde anlamamas\u0131ndan veya dil modelinin kullan\u0131lamamas\u0131ndan kaynakl\u0131 olarak ironiler, de\u011fillemeler (iyi de\u011fil, yok yok, k\u00f6t\u00fc say\u0131lmaz vb.) gibi insan g\u00f6z\u00fcyle rahat anla\u015f\u0131lan duygu durumlar\u0131n\u0131n algoritmalar taraf\u0131ndan anla\u015famamas\u0131 gibi durumlar klasik duygu analizi \u00e7al\u0131\u015fmalar\u0131nda en s\u0131k kar\u015f\u0131la\u015f\u0131lan problemlerdir.\u00a0<\/p>

        Duygu analizi diyaloglarda yap\u0131ld\u0131\u011f\u0131 durumlarda ise duygu de\u011fi\u015fimlerinin tespiti, kar\u015f\u0131 taraf\u0131n konu\u015fmas\u0131n\u0131n bir sonraki konu\u015fma \u00fczerinde yaratt\u0131\u011f\u0131 duygusal etkinin analizi mevcut algoritmalar\u0131n en zay\u0131f yan\u0131d\u0131r.<\/p>

        \u00a0<\/p>

        Makine \u00c7evirisi (Machine Translation)<\/strong><\/h5>

        Bir dilde yaz\u0131lm\u0131\u015f metnin ba\u015fka bir dile bilgisayar algoritmalar\u0131 ile \u00e7evrilmesidir. <\/b>Makine \u00e7evirisi algoritmalar\u0131nda aralar\u0131nda \u00e7eviri yap\u0131lacak idi dili ve iki dil aras\u0131ndaki ili\u015fkileri sistemin anlamas\u0131 gerekmektedir.\u00a0<\/p>

        Bu durum sayesinde makine \u00e7evirisi i\u00e7in e\u011fitilen modeller dil modeli olarak da kullan\u0131labilmektedir. Makine \u00e7evirisi modellerinin e\u011fitimi i\u00e7in iki veya daha fazla dilde ayn\u0131 metnin \u00e7evirilerinden olu\u015fan parekek veri setleri kullan\u0131l\u0131r.<\/p>

        \u00a0<\/p>

        Yapay Metin \u00dcretme (Text Generation)<\/strong><\/h5>

        Bir konu \u00fczerinde bulunan mevcut verilerden \u00e7\u0131kar\u0131mlar yaparak makinenin ayn\u0131 konu \u00fczerine daha \u00f6nce g\u00f6r\u00fclmemi\u015f metinler \u00fcretmesidir.\u00a0<\/b><\/p>

        Bu \u00fcretim i\u015flemi yap\u0131l\u0131rken verilerin kullan\u0131ld\u0131\u011f\u0131 g\u00f6revde sorun yaratacak yanl\u0131\u015f bilgi i\u00e7ermiyor olmas\u0131, do\u011fal dil yap\u0131s\u0131n\u0131n do\u011fru kurulmas\u0131 ve \u00fcretilen verinin birebir kopya olmamas\u0131 en \u00f6nemli noktalard\u0131r. Yapay metin \u00fcretme, NLP g\u00f6revleri i\u00e7in yeterli say\u0131da e\u011fitim verisi bulunmad\u0131\u011f\u0131 durumlarda veri say\u0131s\u0131n\u0131 art\u0131rmak i\u00e7in s\u0131kl\u0131kla kullan\u0131lan bir y\u00f6ntemdir.<\/p>

        \u00a0<\/p>

        Benzerlik (Similarity)<\/strong><\/h5>

        Arama motorlar\u0131 (search engines)<\/b> ba\u015fta olmak \u00fczere pek \u00e7ok alanla kullan\u0131m\u0131 bulunan benzerlik analizi birden fazla metnin benzerlik oranlar\u0131n\u0131 \u00f6l\u00e7erek en alakal\u0131 metinleri bulmay\u0131 veya metinleri benzerliklerine g\u00f6re grupland\u0131rmay\u0131 sa\u011flar.\u00a0<\/p>

        Benzerlik algoritmalar\u0131 ayn\u0131 zamanda daha mikro d\u00fczeyde yaz\u0131m hatalar\u0131n\u0131n d\u00fczeltilmesi gibi temel g\u00f6revlerde de kullan\u0131lmaktad\u0131r. Bu tarz karakter bazl\u0131 benzerlik algoritmalar\u0131nda genellikle hatal\u0131 yaz\u0131lm\u0131\u015f bir kelimenin s\u00f6zl\u00fckte bulunan adaylardan hangi kelimeye en \u00e7ok benzedi\u011fi, i\u00e7erdi\u011fi harflere harf ekleme, \u00e7\u0131karma <\/b>veya i\u015flemleri yap\u0131ld\u0131\u011f\u0131nda en az ka\u00e7 i\u015flem ile di\u011fer kelimeye ula\u015f\u0131labildi\u011fi hesaplanarak \u00f6l\u00e7\u00fcl\u00fcr.\u00a0<\/b><\/p>

        Bu i\u015flemlerde kelime uzunlu\u011fu, karakterin bilgisayar klavyesinde yanl\u0131\u015fl\u0131kla yak\u0131ndaki bir harfle kar\u0131\u015ft\u0131r\u0131lm\u0131\u015f olma ihtimali gibi durumlar da dikkate al\u0131nmaktad\u0131r.\u00a0<\/p>

        Birden fazla metni k\u0131yaslayan algoritmalara geri d\u00f6nersek<\/b> istatistiksel \u00e7al\u0131\u015fan benzerlik algoritmalar\u0131 genellikle metin i\u00e7inde ge\u00e7en Ngramlar\u0131n \u00f6rt\u00fc\u015fme oranlar\u0131na g\u00f6re bir benzerlik oran\u0131 hesaplar.\u00a0<\/p>

        Son zamanlarda kullan\u0131m\u0131 pop\u00fcler olan kelime vekt\u00f6rleri<\/b> bu alanda yap\u0131lan \u00e7al\u0131\u015fmalarda terimsel olarak \u00f6rt\u00fc\u015fmezse de anlamsal olarak \u00f6rt\u00fc\u015fen metinler i\u00e7in daha do\u011fru benzerlik skorlar\u0131 hesaplanmas\u0131na katk\u0131 sa\u011flam\u0131\u015ft\u0131r.<\/p>

        \u00a0<\/p>

        \u00d6zetleme (Summarization)<\/strong><\/h5>

        Bir metnin \u00f6nemli noktalar\u0131n\u0131 belirtecek ve bilgi eksikli\u011fine neden olmayacak \u015fekilde k\u0131salt\u0131lmas\u0131 i\u015flemidir.<\/b>\u00a0<\/p>

        \u00d6zetleme algoritmalar\u0131n\u0131n bir k\u0131sm\u0131 paragraf i\u00e7inde \u00f6neli c\u00fcmlelerin aynen filtrelenmesi \u015feklinde, bir k\u0131sm\u0131 ise t\u00fcm paragraf\u0131n algoritma taraf\u0131ndan anla\u015f\u0131l\u0131p dil modelleri deste\u011fi ile \u00f6nemli k\u0131s\u0131mlar\u0131n yorumlanarak \u00f6zg\u00fcn bir veya birka\u00e7 c\u00fcmle olarak k\u0131salt\u0131lmas\u0131 \u015feklinde \u00e7al\u0131\u015fmaktad\u0131r.<\/p>

        \u00a0<\/p>

        \u0130simlendirilmi\u015f Varl\u0131k Tespiti (Named Entity Recognition)<\/strong><\/h5>

        Metin i\u00e7erisinde ge\u00e7en varl\u0131klar\u0131n belirlenmesi i\u015flemidir. <\/b>Tespit edilmeye \u00e7al\u0131\u015f\u0131lan varl\u0131klar \u00f6zel isimler, yer isimleri ,organizasyon isimleri, tarihler, para birimleri gibi pek \u00e7ok farkl\u0131 gruptan se\u00e7ilebilir.<\/p>

        \u00a0<\/p>

        Niyet Tespiti (Intent Recognition)<\/strong><\/h5>

        Chatbot\u2019lar<\/b> ve ak\u0131ll\u0131 asistanlar <\/b>ba\u015fta olmak \u00fczere pek \u00e7ok alanda \u00f6nemli i\u015flev g\u00f6ren niyet tespiti bir metinde bulunan ifadenin pragmatik analizini yaparak metin sahibinin bu ifade ile ne ifade etmeye \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 veya ne elde etmek istedi\u011fini ortaya \u00e7\u0131kar\u0131r.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

        NLP (Natural Language Processing), T\u00fcrk\u00e7e ismiyle Do\u011fal Dil \u0130\u015fleme (DD\u0130), yapay zekan\u0131n alt\u0131nda yer alan ve amac\u0131 insanlar\u0131n \u00fcretti\u011fi do\u011fal dilin i\u015flenebilmesi, anla\u015f\u0131labilmesi, analiz edilerek \u00e7\u0131kt\u0131lara d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilmesini sa\u011flayan bir metin analiti\u011fi ve ara\u015ft\u0131rma alan\u0131d\u0131r. G\u00fcnl\u00fck hayatta s\u0131k\u00e7a ba\u015fvurdu\u011fumuz ak\u0131ll\u0131 asistanlar, dil \u00e7eviri uygulamalar\u0131, \u00e7a\u011fr\u0131 merkezlerinde kullan\u0131lan IVR (sesli yan\u0131t) uygulamalar\u0131 arkas\u0131nda Do\u011fal Dil \u0130\u015fleme teknolojilerini […]<\/p>\n","protected":false},"author":1,"featured_media":2700,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[110],"tags":[],"sektor":[133],"class_list":["post-2616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-post","sektor-cx-genel"],"yoast_head":"\nDo\u011fal Dil \u0130\u015fleme (NLP) Nedir? | Artiwise Blog<\/title>\n<meta name=\"description\" content=\"Do\u011fal Dil \u0130\u015fleme (NLP) teknolojisinin temellerini ve uygulama alanlar\u0131n\u0131 ke\u015ffedin. 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