{"id":28553,"date":"2023-08-30T18:51:01","date_gmt":"2023-08-30T16:51:01","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/?p=28553"},"modified":"2023-08-30T18:51:01","modified_gmt":"2023-08-30T16:51:01","slug":"what-are-nlp-algorithms","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-are-nlp-algorithms\/","title":{"rendered":"What are NLP Algorithms? A Guide to Natural Language Processing"},"content":{"rendered":"<p><span style=\"font-weight: 400;\"><strong>Have you ever wished to know more about natural language processing (NLP) algorithms?<\/strong> <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Are you intrigued by how NLP works to help data analysts gain valuable insights from customer data?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With the recent advancements in artificial intelligence (AI) and <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-machine-learning\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">machine learning<\/span><\/a><span style=\"font-weight: 400;\">, understanding how natural language processing works is becoming increasingly important.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this guide, we&#8217;ll discuss what NLP algorithms are, how they work, and the different types available for businesses to use.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We&#8217;ll cover:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#what-is-natural-langauge-processing\"><span style=\"font-weight: 400;\">What is natural language processing (NLP)?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#what-are-nlp-algorithms\"><span style=\"font-weight: 400;\">What are NLP algorithms?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#types-of-nlp-algorithms\"><span style=\"font-weight: 400;\">Types of NLP algorithms<\/span><\/a>\n<ul>\n<li aria-level=\"1\"><a href=\"#sentiment-analysis\"><span style=\"font-weight: 400;\">Sentiment analysis<\/span><\/a><\/li>\n<li aria-level=\"1\"><a href=\"#keyword-extraction\"><span style=\"font-weight: 400;\">Keyword extraction<\/span><\/a><\/li>\n<li aria-level=\"1\"><a href=\"#knowledge-graphs\"><span style=\"font-weight: 400;\">Knowledge graphs<\/span><\/a><\/li>\n<li aria-level=\"1\"><a href=\"#word-clouds\"><span style=\"font-weight: 400;\">Word clouds<\/span><\/a><\/li>\n<li aria-level=\"1\"><a href=\"#text-summarization\"><span style=\"font-weight: 400;\">Text summarization<\/span><\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#how-to-get-started-with-nlp-algorithms\"><span style=\"font-weight: 400;\">How to get started with NLP algorithms<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#nlp-algorithms-faq\"><span style=\"font-weight: 400;\">NLP algorithms FAQs<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#wrap-up\"><span style=\"font-weight: 400;\">Wrap-up<\/span><\/a><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Ready to learn more about NLP algorithms and how to get started with them? Let&#8217;s dive in.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">1. What is Natural Language Processing (NLP)?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Natural Language Processing (NLP) is a branch of AI that focuses on developing computer algorithms to understand and process natural language.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It allows computers to understand human written and spoken language to analyze text, extract meaning, recognize patterns, and generate new text content.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To fully understand NLP, you&#8217;ll have to know what their algorithms are and what they involve. Next, I\u2019ll provide a simple explanation of them.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">2. What are NLP algorithms?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">NLP algorithms are complex mathematical formulas used to train computers to understand and process natural language. They help machines make sense of the data they get from written or spoken words and extract meaning from them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Put in simple terms, these algorithms are like dictionaries that allow machines to make sense of what people are saying without having to understand the intricacies of human language.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">NLP algorithms use a variety of techniques, such as sentiment analysis, keyword extraction, knowledge graphs, word clouds, and text summarization, which we&#8217;ll discuss in the next section.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">3. Types of NLP algorithms<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To help achieve the different results and applications in NLP, a range of algorithms are used by data scientists.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Some of the common types of NLP algorithms in <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-science\/\"><span style=\"font-weight: 400;\">data science<\/span><\/a><span style=\"font-weight: 400;\"> include:<\/span><\/p>\n<h3 id=\"sentiment-analysis\"><span style=\"font-weight: 400;\">Sentiment analysis<\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-models\/\" target=\"_blank\" rel=\"noopener\">Sentiment analysis is the process of classifying text<\/a> into categories of positive, negative, or neutral sentiment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It works through the use of several techniques:<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">1. Tokenization<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">This is the first step in the process, where the text is broken down into individual words or &#8220;tokens&#8221;. Each token is then analyzed separately.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">2. Stop words removal<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Stop words such as &#8220;is&#8221;, &#8220;an&#8221;, and &#8220;the&#8221;, which do not carry significant meaning, are removed to focus on important words.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">3. Text normalization<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Words are converted to their base or root form. For example, \u201crunning\u201d might be reduced to its root word, \u201crun\u201d. This is known as Stemming or Lemmatization.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">4. Feature extraction<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Key features or words that will help determine sentiment are extracted from the text. These could include adjectives like &#8220;good&#8221;, &#8220;bad&#8221;, &#8220;awesome&#8221;, etc.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">5. Classification<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The sentiment is then classified using machine learning algorithms. This could be a binary classification (positive\/negative), a multi-class classification (happy, sad, angry, etc.), or a scale (rating from 1 to 10).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, sarcasm, irony, slang, and other factors can make it challenging to determine sentiment accurately.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Nonetheless, it&#8217;s often used by businesses to gauge customer sentiment about their products or services through customer feedback.<\/span><\/p>\n<h3 id=\"keyword-extraction\"><span style=\"font-weight: 400;\">Keyword extraction<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Keyword extraction is a process of extracting important keywords or phrases from text.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This algorithm extracts meaningful keywords from text to help identify the topics or trends. It can be used to identify topics in documents, blog posts, and web pages.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This can be further applied to business use cases by monitoring customer conversations and identifying potential market opportunities.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It&#8217;s also typically used in situations where large amounts of unstructured text data need to be analyzed.<\/span><\/p>\n<h3 id=\"knowledge-graphs\"><span style=\"font-weight: 400;\">Knowledge graph<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This algorithm creates a graph network of important entities, such as people, places, and things. This graph can then be used to understand how different concepts are related.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A knowledge graph is a key algorithm in helping machines understand the context and semantics of human language. This means that machines are able to understand the nuances and complexities of language.<\/span><\/p>\n<h3 id=\"word-clouds\"><span style=\"font-weight: 400;\">Word cloud<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This one most of us have come across at one point or another! A word cloud is a graphical representation of the frequency of words used in the text. It can be used to identify trends and topics in customer feedback.<\/span><\/p>\n<p>Just in case, here&#8217;s a word cloud composed using the text from this article as the data:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-28654\" src=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2023\/08\/nlp-word-cloud-article.png\" alt=\"A word cloud with the data being the text of this NLP algorithms article.\" width=\"1062\" height=\"535\" title=\"\" srcset=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2023\/08\/nlp-word-cloud-article.png 1062w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2023\/08\/nlp-word-cloud-article-300x151.png 300w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2023\/08\/nlp-word-cloud-article-1024x516.png 1024w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2023\/08\/nlp-word-cloud-article-768x387.png 768w\" sizes=\"auto, (max-width: 1062px) 100vw, 1062px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Word clouds are commonly used for analyzing data from social network websites, customer reviews, feedback, or other textual content to get insights about prominent themes, sentiments, or buzzwords around a particular topic.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They&#8217;re commonly used in presentations to give an intuitive summary of the text.<\/span><\/p>\n<h3 id=\"text-summarization\"><span style=\"font-weight: 400;\">Text summarization<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This algorithm creates summaries of long texts to make it easier for humans to understand their contents quickly. Businesses can use it to summarize customer feedback or large documents into shorter versions for better analysis.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Using these algorithms, data professionals can perform common data analytics tasks used in businesses.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">3 common use cases for NLP algorithms<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">To give you a better idea of what these algorithms can offer for business applications, here are three common use cases for NLP algorithms:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Customer support<\/b><span style=\"font-weight: 400;\">: Businesses can use sentiment analysis to monitor customer feedback and identify areas of improvement.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Market analysis<\/b><span style=\"font-weight: 400;\">: Keyword extraction can help businesses identify topics and trends in customer conversations to inform their marketing strategies.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Text summarization<\/b><span style=\"font-weight: 400;\">: Businesses can use text summarization to quickly analyze long documents or customer feedback.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">These are just a few of the ways businesses can use NLP algorithms to gain insights from their data.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">4. How to get started with NLP algorithms<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Interested to try out some of these algorithms for yourself? Here are a few steps on how to get started.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 1: Determine your problem<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Before you start, it&#8217;s important to define your business problem.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This involves asking questions like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What data do you have?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which insights are you looking for?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Try to be as specific as possible. This will help with selecting the appropriate algorithm later on.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 2: Identify your dataset<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The next step is to identify your dataset. Depending on the problem you are trying to solve, you might have access to customer feedback data, product reviews, forum posts, or social media data.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 3: Data cleaning<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Once you have identified your dataset, you&#8217;ll have to prepare the data by cleaning it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data cleaning involves removing any irrelevant data or typo errors, converting all text to lowercase, and normalizing the language. This step might require some knowledge of common libraries in <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-python\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Python<\/span><\/a><span style=\"font-weight: 400;\"> or packages in R. If you need a refresher, just use <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-cleaning\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">our guide to data cleaning<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You can use NLP libraries such as <\/span><a href=\"https:\/\/www.nltk.org\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">NLTK<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/spacy.io\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">spaCy<\/span><\/a><span style=\"font-weight: 400;\"> to help clean your data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Some other common tools for cleaning data include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/textblob.readthedocs.io\/en\/dev\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">TextBlob<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/scikit-learn.org\/stable\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Scikit-learn<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/stanfordnlp.github.io\/CoreNLP\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Stanford CoreNLP<\/span><\/a><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">These are just among the many <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-tools\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">machine learning tools<\/span><\/a><span style=\"font-weight: 400;\"> used by data scientists.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 4: Select an algorithm<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">For your next step, you&#8217;ll need to select an algorithm. This will depend on the business problem you are trying to solve. You can refer to the list of algorithms we discussed earlier for more information.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Once you have identified the algorithm, you&#8217;ll need to train it by feeding it with the data from your dataset.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You can use the Scikit-learn library in Python, which offers a variety of algorithms and tools for natural language processing.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 5: Analyze output results<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The last step is to analyze the output results of your algorithm. Depending on what type of algorithm you are using, you might see metrics such as sentiment scores or keyword frequencies.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here are some common metrics used to evaluate outputs:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Precision:<\/strong> Measures how accurate the algorithm is at correctly classifying data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Recall:<\/strong> Measures how much of the relevant data was correctly classified.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>F1 score:<\/strong> Measures balance between precision and recall.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">You can also use visualizations such as word clouds to better present your results to stakeholders.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">5. NLP algorithms FAQs<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Which programming language is best for NLP?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Python is the best programming language for NLP for its wide range of NLP libraries, ease of use, and community support. However, other programming languages like R and Java are also popular for NLP.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Is NLP high-paying?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Yes, NLP data scientists are paid high-paying salaries. According to PayScale, the average salary for an NLP data scientist in the U.S. is about<\/span> <a href=\"https:\/\/www.payscale.com\/research\/US\/Job=Data_Scientist\/Salary\/22af3f0b\/Natural-Language-Processing-NLP\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">$104,000 per year<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Can Python be used for NLP?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Yes, Python can be used for NLP. It&#8217;s the most popular due to its wide range of libraries and tools. It is also considered one of the most beginner-friendly programming languages which makes it ideal for beginners to learn NLP.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">6. Wrap-up<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">NLP algorithms can sound like far-fetched concepts, but in reality, with the right directions and the determination to learn, you can easily get started with them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We hope this guide gives you a better overall understanding of what natural language processing (NLP) algorithms are. To recap, we discussed the different types of NLP algorithms available, as well as their common use cases and applications.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you&#8217;re interested in getting started with NLP algorithms, check out CareerFoundry&#8217;s <\/span><strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\">free, 5-day data analytics short course<\/a><\/strong><span style=\"font-weight: 400;\">. For more related information on data analytics and generative AI, do check out the following articles:<\/span><\/p>\n<ul>\n<li><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-models\/\">A Beginner\u2019s Guide to Machine Learning Models<\/a><\/li>\n<li><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analysis-prompts\/\">11 of the Best ChatGPT Data Analysis Prompts You Should Know<\/a><\/li>\n<li><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-engineer-salary\/\">What\u2019s the Average Machine Learning Engineer Salary?<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>NLP algorithms are the power behind the LLMs fueling the current generative AI revolution. Learn how NLP works, and how data analysts can use it.<\/p>\n","protected":false},"author":159,"featured_media":28653,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"yes","_lmt_disable":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-28553","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics"],"acf":{"homepage_category_featured":false,"cards_inner_programs_lists_left":"","cards_inner_programs_lists_right":"","related_plan_cards":""},"modified_by":"Rash SEO","_links":{"self":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/28553","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/users\/159"}],"replies":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/comments?post=28553"}],"version-history":[{"count":5,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/28553\/revisions"}],"predecessor-version":[{"id":28669,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/28553\/revisions\/28669"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media\/28653"}],"wp:attachment":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media?parent=28553"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/categories?post=28553"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/tags?post=28553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}