{"id":478345,"date":"2023-08-09T09:31:27","date_gmt":"2023-08-09T09:31:27","guid":{"rendered":""},"modified":"2023-09-05T11:16:36","modified_gmt":"2023-09-05T11:16:36","slug":"part-of-speech-pos-tagging","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/part-of-speech-pos-tagging\/","title":{"rendered":"Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) etiketleme"},"content":{"rendered":"<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketlemenin K\u00f6keni ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Dilbilgisi etiketlemesi olarak da bilinen Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) etiketlemesi, belirli bir metindeki her kelimeye belirli bir dilbilgisi kategorisi veya konu\u015fman\u0131n bir b\u00f6l\u00fcm\u00fcn\u00fc atamak i\u00e7in kullan\u0131lan \u00f6nemli bir do\u011fal dil i\u015fleme (NLP) tekni\u011fidir. POS etiketleme kavram\u0131n\u0131n k\u00f6keni, hesaplamal\u0131 dilbilim ve dil i\u015fleme ara\u015ft\u0131rmalar\u0131n\u0131n ilk g\u00fcnlerine kadar uzanabilir.<\/p>\n<p>POS etiketlemenin ilk s\u00f6z\u00fc, ara\u015ft\u0131rmac\u0131lar\u0131n bilgisayarlar\u0131 kullanarak metni i\u015fleme ve analiz etmenin yollar\u0131n\u0131 ke\u015ffetmeye ba\u015flad\u0131klar\u0131 1950&#039;lere kadar uzan\u0131yor. POS etiketlemeye y\u00f6nelik ilk giri\u015fimlerden biri Zellig Harris&#039;in 1954&#039;teki \u00e7al\u0131\u015fmas\u0131na atfedilebilir; burada \u0130ngilizce c\u00fcmlelerdeki isim c\u00fcmlelerini ve fiil c\u00fcmlelerini tan\u0131mlamak i\u00e7in basit istatistiksel teknikler kulland\u0131.<\/p>\n<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketleme Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) etiketlemesi, dilin i\u015flenmesinde ve anla\u015f\u0131lmas\u0131nda temel bir rol oynar. Bilgi alma, duygu analizi, makine \u00e7evirisi ve konu\u015fma tan\u0131ma gibi \u00e7e\u015fitli NLP g\u00f6revlerinde kritik bir ad\u0131md\u0131r. POS etiketleme, bilgisayarlar\u0131n bir c\u00fcmlenin gramer yap\u0131s\u0131n\u0131 kavramas\u0131n\u0131 sa\u011flar; bu da dilin do\u011fru anla\u015f\u0131lmas\u0131 i\u00e7in \u00e7ok \u00f6nemlidir.<\/p>\n<p>POS etiketlemenin temel amac\u0131, belirli bir metindeki her kelimeye isim, fiil, s\u0131fat, zarf, zamir, edat, ba\u011fla\u00e7 ve \u00fcnlem gibi belirli bir konu\u015fma b\u00f6l\u00fcm\u00fc kategorisine atamakt\u0131r. Bu bilgi, bir c\u00fcmledeki her kelimenin s\u00f6zdizimsel rol\u00fcn\u00fcn belirlenmesine yard\u0131mc\u0131 olur ve daha ileri analizler i\u00e7in daha kapsaml\u0131 bir dilsel model olu\u015fturulmas\u0131na katk\u0131da bulunur.<\/p>\n<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketlemenin \u0130\u00e7 Yap\u0131s\u0131: Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>POS etiketleme genellikle kurala dayal\u0131 y\u00f6ntemler veya istatistiksel y\u00f6ntemler kullan\u0131larak ger\u00e7ekle\u015ftirilir. Kurala dayal\u0131 etiketlemede, dil kurallar\u0131, bir kelimenin konu\u015fman\u0131n bir k\u0131sm\u0131n\u0131 ba\u011flam\u0131na ve kom\u015fu kelimelere g\u00f6re tan\u0131mlamak i\u00e7in tan\u0131mlan\u0131r. \u00d6te yandan, istatistiksel etiketleme, belirli bir kelime i\u00e7in konu\u015fman\u0131n en olas\u0131 b\u00f6l\u00fcm\u00fcn\u00fc tahmin eden olas\u0131l\u0131ksal bir model olu\u015fturmak i\u00e7in \u00f6nceden etiketlenmi\u015f e\u011fitim verilerine dayan\u0131r.<\/p>\n<p>POS etiketleme s\u00fcreci birka\u00e7 ad\u0131mdan olu\u015fur:<\/p>\n<ol>\n<li>Belirte\u00e7le\u015ftirme: Giri\u015f metni ayr\u0131 kelimelere veya belirte\u00e7lere b\u00f6l\u00fcn\u00fcr.<\/li>\n<li>S\u00f6zc\u00fcksel Analiz: Her kelime kendi lemmas\u0131 veya temel formuyla e\u015fle\u015ftirilir.<\/li>\n<li>Ba\u011flamsal Analiz: Mevcut kelimeye uygun etiketi belirlemek i\u00e7in \u00e7evredeki kelimeler ve bunlar\u0131n konu\u015fma b\u00f6l\u00fcm\u00fc etiketleri dikkate al\u0131n\u0131r.<\/li>\n<li>Belirsizli\u011fi giderme: Belirsizlik durumlar\u0131nda istatistiksel modeller veya kural tabanl\u0131 algoritmalar do\u011fru etiketin se\u00e7ilmesine yard\u0131mc\u0131 olur.<\/li>\n<\/ol>\n<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketlemenin Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>POS etiketlemenin temel \u00f6zellikleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li>Dilsel Anlama: POS etiketleme, bilgisayar\u0131n bir c\u00fcmlenin gramer yap\u0131s\u0131n\u0131 kavrama yetene\u011fini geli\u015ftirerek dilin anla\u015f\u0131lmas\u0131n\u0131 geli\u015ftirir.<\/li>\n<li>Bilgi Eri\u015fimi: POS etiketleme, arama terimlerinin s\u00f6zdizimsel ba\u011flam\u0131na dayal\u0131 olarak daha do\u011fru arama sonu\u00e7lar\u0131 sa\u011flayarak bilgi al\u0131m\u0131na yard\u0131mc\u0131 olur.<\/li>\n<li>Metinden Konu\u015fmaya Sentez: Konu\u015fma sentezi sistemlerinde POS etiketleme, daha do\u011fal ve ba\u011flamsal olarak uygun konu\u015fman\u0131n \u00fcretilmesine yard\u0131mc\u0131 olur.<\/li>\n<li>Makine \u00c7evirisi: POS etiketleri, makine \u00e7evirisi g\u00f6revlerinde de\u011ferli bilgiler sa\u011flayarak \u00e7evrilmi\u015f metinlerin do\u011frulu\u011funu ve ak\u0131c\u0131l\u0131\u011f\u0131n\u0131 art\u0131r\u0131r.<\/li>\n<\/ul>\n<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketleme T\u00fcrleri: Kapsaml\u0131 Bir Genel Bak\u0131\u015f<\/h2>\n<p>POS etiketleme, kullan\u0131lan dillere, etiket k\u00fcmelerine ve y\u00f6ntemlere ba\u011fl\u0131 olarak \u00e7e\u015fitli t\u00fcrlere ayr\u0131labilir. Yayg\u0131n POS etiketleme t\u00fcrlerinden baz\u0131lar\u0131 \u015funlard\u0131r:<\/p>\n<ol>\n<li>\n<p>Kural Tabanl\u0131 Etiketleme:<\/p>\n<ul>\n<li>Kelimeleri ba\u011flama g\u00f6re etiketlemek i\u00e7in bir dizi dil kural\u0131 tan\u0131mlanm\u0131\u015ft\u0131r.<\/li>\n<li>Kurallar\u0131n manuel olarak olu\u015fturulmas\u0131 zaman al\u0131c\u0131d\u0131r ancak belirli alanlar i\u00e7in olduk\u00e7a do\u011fru sonu\u00e7lar verebilir.<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Stokastik Etiketleme:<\/p>\n<ul>\n<li>E\u011fitim verilerine dayal\u0131 olarak etiket atamak i\u00e7in Gizli Markov Modelleri (HMM) veya Ko\u015fullu Rastgele Alanlar (CRF) gibi olas\u0131l\u0131ksal modelleri kullan\u0131r.<\/li>\n<li>\u0130statistiksel y\u00f6ntemler farkl\u0131 dillere ve alanlara iyi uyum sa\u011flar.<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>D\u00f6n\u00fc\u015f\u00fcm Tabanl\u0131 Etiketleme:<\/p>\n<ul>\n<li>Etiketleme do\u011frulu\u011funu yinelemeli olarak geli\u015ftirmek i\u00e7in bir dizi d\u00f6n\u00fc\u015f\u00fcmsel kural kullan\u0131r.<\/li>\n<li>D\u00f6n\u00fc\u015f\u00fcm Tabanl\u0131 \u00d6\u011frenme (TBL) bu yakla\u015f\u0131m\u0131n bir \u00f6rne\u011fidir.<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Hibrit Etiketleme:<\/p>\n<ul>\n<li>\u0130lgili g\u00fc\u00e7l\u00fc y\u00f6nlerden yararlanmak i\u00e7in birden fazla etiketleme y\u00f6ntemini birle\u015ftirir.<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Dile \u00d6zel Etiketleme:<\/p>\n<ul>\n<li>Farkl\u0131 diller, dilsel n\u00fcanslar\u0131 ele almak i\u00e7in dile \u00f6zg\u00fc etiket k\u00fcmeleri ve kurallar gerektirebilir.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketlemeyi Kullanma Yollar\u0131: Zorluklar ve \u00c7\u00f6z\u00fcmler<\/h2>\n<p>POS etiketleme, a\u015fa\u011f\u0131dakiler gibi \u00e7e\u015fitli alanlarda uygulama alan\u0131 bulur:<\/p>\n<ul>\n<li>Bilgi \u00c7\u0131karma: POS etiketleri, yap\u0131land\u0131r\u0131lmam\u0131\u015f metinden belirli bilgilerin \u00e7\u0131kar\u0131lmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<li>Duyarl\u0131l\u0131k Analizi: POS ba\u011flam\u0131n\u0131 anlamak, daha do\u011fru duyarl\u0131l\u0131k analizi sonu\u00e7lar\u0131na katk\u0131da bulunur.<\/li>\n<li>Adland\u0131r\u0131lm\u0131\u015f Varl\u0131k Tan\u0131ma: POS etiketleme, metinlerdeki adland\u0131r\u0131lm\u0131\u015f varl\u0131klar\u0131n tan\u0131mlanmas\u0131nda yard\u0131mc\u0131 olur.<\/li>\n<\/ul>\n<p>Ancak POS etiketlemenin de zorluklar\u0131 vard\u0131r:<\/p>\n<ul>\n<li>Belirsizlik: Baz\u0131 kelimelerin birden fazla potansiyel etiketi olabilir, bu da etiketlemede belirsizli\u011fe yol a\u00e7ar.<\/li>\n<li>Kelime Da\u011farc\u0131\u011f\u0131 D\u0131\u015f\u0131ndaki Kelimeler: E\u011fitim verilerinde bulunmayan kelimeler, g\u00f6r\u00fcnmeyen kelimelerin etiketlenmesinde zorluk yaratabilir.<\/li>\n<li>\u00c7ok Dilde Etiketleme: Farkl\u0131 diller, dile \u00f6zg\u00fc modeller ve etiket k\u00fcmeleri gerektirir.<\/li>\n<\/ul>\n<p>Bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in ara\u015ft\u0131rmac\u0131lar, etiketleme algoritmalar\u0131n\u0131 s\u00fcrekli olarak geli\u015ftiriyor, daha b\u00fcy\u00fck ve daha \u00e7e\u015fitli e\u011fitim veri k\u00fcmeleri olu\u015fturuyor ve daha iyi genelleme i\u00e7in sinir a\u011f\u0131 tabanl\u0131 yakla\u015f\u0131mlar\u0131 ara\u015ft\u0131r\u0131yor.<\/p>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketleme<\/th>\n<th>Adland\u0131r\u0131lm\u0131\u015f Varl\u0131k Tan\u0131ma (NER)<\/th>\n<th>S\u00f6zdizimsel Ayr\u0131\u015ft\u0131rma<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ama\u00e7<\/td>\n<td>Kelime kategorileri atama<\/td>\n<td>Adland\u0131r\u0131lm\u0131\u015f varl\u0131klar\u0131 tan\u0131mlama<\/td>\n<td>S\u00f6zdizimi analiz ediliyor<\/td>\n<\/tr>\n<tr>\n<td>Odak<\/td>\n<td>Gramer yap\u0131s\u0131<\/td>\n<td>\u00d6zel isimler ve varl\u0131klar<\/td>\n<td>C\u00fcmle yap\u0131s\u0131<\/td>\n<\/tr>\n<tr>\n<td>Uygulamalar<\/td>\n<td>NLP, Bilgi alma<\/td>\n<td>Bilgi \u00e7\u0131karma<\/td>\n<td>Dil anlay\u0131\u015f\u0131<\/td>\n<\/tr>\n<tr>\n<td>Metodoloji<\/td>\n<td>Kural Tabanl\u0131 veya \u0130statistiksel<\/td>\n<td>\u0130statistiksel ve kural tabanl\u0131<\/td>\n<td>S\u00f6zdizimi tabanl\u0131 ayr\u0131\u015ft\u0131rma<\/td>\n<\/tr>\n<tr>\n<td>\u00c7\u0131kt\u0131<\/td>\n<td>Her kelime i\u00e7in POS etiketleri<\/td>\n<td>Tan\u0131mlanan adland\u0131r\u0131lm\u0131\u015f varl\u0131klar<\/td>\n<td>A\u011fa\u00e7 ayr\u0131\u015ft\u0131rma<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketlemeyle \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Teknoloji ilerledik\u00e7e POS etiketlemenin daha do\u011fru ve verimli hale gelmesi bekleniyor. Gelecekteki potansiyel geli\u015fmelerden baz\u0131lar\u0131 \u015funlard\u0131r:<\/p>\n<ul>\n<li>Sinir A\u011f\u0131 Tabanl\u0131 Yakla\u015f\u0131mlar: Etiketleme performans\u0131n\u0131 art\u0131rmak ve dil karma\u015f\u0131kl\u0131klar\u0131n\u0131n \u00fcstesinden gelmek i\u00e7in derin \u00f6\u011frenmeden ve sinir a\u011flar\u0131ndan yararlanmak.<\/li>\n<li>Diller Aras\u0131 Etiketleme: \u00c7ok dilli POS etiketleme i\u00e7in diller aras\u0131nda bilgi aktarabilen modeller geli\u015ftirmek.<\/li>\n<li>Ger\u00e7ek Zamanl\u0131 Etiketleme: Canl\u0131 transkripsiyon ve sohbet robotlar\u0131 gibi ger\u00e7ek zamanl\u0131 uygulamalar i\u00e7in POS etiketleme algoritmalar\u0131n\u0131n optimize edilmesi.<\/li>\n<\/ul>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) Etiketleme ile Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular\u0131, POS etiketlemeyi i\u00e7eren veri alma ve i\u015fleme g\u00f6revlerinde hayati bir rol oynar. Proxy sunucular\u0131, istemciler ve web sunucular\u0131 aras\u0131nda arac\u0131 g\u00f6revi g\u00f6rerek kullan\u0131c\u0131lar\u0131n farkl\u0131 IP adresleri ve konumlar arac\u0131l\u0131\u011f\u0131yla web kaynaklar\u0131na eri\u015fmesine olanak tan\u0131r. POS etiketleme i\u00e7in proxy sunucular a\u015fa\u011f\u0131daki \u015fekillerde kullan\u0131labilir:<\/p>\n<ol>\n<li>Veri Kaz\u0131ma: Proxy sunucular\u0131, \u00e7e\u015fitli kaynaklardan \u00e7e\u015fitli ve kapsaml\u0131 metin verilerinin toplanmas\u0131n\u0131 sa\u011flar; bu, kapsaml\u0131 POS etiketleme modelleri olu\u015fturmak i\u00e7in gereklidir.<\/li>\n<li>\u00c7ok Dilde Etiketleme: Proxy sunucular\u0131 sayesinde ara\u015ft\u0131rmac\u0131lar farkl\u0131 dil b\u00f6lgelerindeki metinlere eri\u015febilir ve bunlar\u0131 i\u015fleyebilir, bu da \u00e7ok dilli POS etiketleme ara\u015ft\u0131rmas\u0131na yard\u0131mc\u0131 olabilir.<\/li>\n<li>Y\u00fck Dengeleme: Proxy sunucular, etiketleme i\u015f y\u00fck\u00fcn\u00fc birden fazla sunucuya da\u011f\u0131tarak verimli ve g\u00fcvenilir POS etiketleme hizmetleri sa\u011flar.<\/li>\n<\/ol>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) etiketleme ve uygulamalar\u0131 hakk\u0131nda daha fazla bilgi i\u00e7in a\u015fa\u011f\u0131daki kaynaklar\u0131 ke\u015ffedebilirsiniz:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.nltk.org\/\" target=\"_new\" rel=\"noopener nofollow\">Do\u011fal Dil Ara\u00e7 Seti (NLTK)<\/a><\/li>\n<li><a href=\"https:\/\/nlp.stanford.edu\/\" target=\"_new\" rel=\"noopener nofollow\">Stanford NLP<\/a><\/li>\n<li><a href=\"https:\/\/spacy.io\/\" target=\"_new\" rel=\"noopener nofollow\">uzay<\/a><\/li>\n<li><a href=\"https:\/\/opennlp.apache.org\/\" target=\"_new\" rel=\"noopener nofollow\">OpenNLP<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/text\" target=\"_new\" rel=\"noopener nofollow\">TensorFlow NLP<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy<\/a><\/li>\n<\/ul>\n<p>Sonu\u00e7 olarak, Konu\u015fma B\u00f6l\u00fcm\u00fc (POS) etiketleme, do\u011fal dil i\u015flemenin \u00e7ok \u00f6nemli bir bile\u015fenidir ve bilgisayarlar\u0131n dil yap\u0131s\u0131n\u0131 ve anlam\u0131n\u0131 daha iyi anlamas\u0131n\u0131 sa\u011flar. Teknolojideki ilerlemeler ve proxy sunucular\u0131n yard\u0131m\u0131yla POS etiketleme, gelecekte dille ilgili \u00e7e\u015fitli uygulamalarda daha da \u00f6nemli bir rol oynamaya haz\u0131rlan\u0131yor.<\/p>","protected":false},"featured_media":469119,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478345","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Part-of-Speech (POS) Tagging: Enhancing Language Understanding<\/mark>","faq_items":[{"question":"What is Part-of-Speech (POS) tagging?","answer":"<p>Part-of-Speech (POS) tagging is a natural language processing technique that assigns specific grammatical categories, or parts of speech, to each word in a given text. It helps computers understand the syntactic role of words in sentences, leading to better language comprehension and analysis.<\/p>"},{"question":"How did Part-of-Speech (POS) tagging originate?","answer":"<p>The concept of POS tagging dates back to the 1950s, with early attempts made by Zellig Harris in 1954. He used statistical methods to identify noun phrases and verb phrases in English sentences, marking the beginning of POS tagging research.<\/p>"},{"question":"How does Part-of-Speech (POS) tagging work?","answer":"<p>POS tagging involves tokenization, lexical analysis, contextual analysis, and disambiguation. Words in a text are divided into tokens, matched with their base forms, and tagged based on surrounding words and probabilistic models or rule-based algorithms.<\/p>"},{"question":"What are the key features of Part-of-Speech (POS) tagging?","answer":"<p>The key features include enhanced linguistic understanding, improved information retrieval, better text-to-speech synthesis, and increased accuracy in machine translation tasks.<\/p>"},{"question":"What types of Part-of-Speech (POS) tagging exist?","answer":"<p>There are several types of POS tagging, including rule-based tagging, stochastic tagging, transformation-based tagging, hybrid tagging, and language-specific tagging, each with its own strengths and applications.<\/p>"},{"question":"How is Part-of-Speech (POS) tagging used and what challenges does it face?","answer":"<p>POS tagging finds applications in information extraction, sentiment analysis, and named entity recognition. Some challenges include word ambiguity, handling out-of-vocabulary words, and dealing with multilingual text.<\/p>"},{"question":"How does the future of Part-of-Speech (POS) tagging look like?","answer":"<p>The future of POS tagging holds promise with neural network-based approaches, cross-lingual tagging, and real-time applications being developed to improve accuracy and efficiency.<\/p>"},{"question":"How are proxy servers associated with Part-of-Speech (POS) tagging?","answer":"<p>Proxy servers, like OneProxy, play a crucial role in data retrieval for POS tagging. They enable access to diverse text sources, multilingual texts, and facilitate load balancing for efficient tagging services.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/478345","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/478345\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/469119"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=478345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}