{"id":163601,"date":"2022-09-13T17:56:12","date_gmt":"2022-09-13T16:56:12","guid":{"rendered":"https:\/\/datascientest.com\/de\/?p=163601"},"modified":"2024-01-23T17:19:25","modified_gmt":"2024-01-23T16:19:25","slug":"word-embedding","status":"publish","type":"post","link":"https:\/\/datascientest.com\/de\/word-embedding","title":{"rendered":"Das Word Embedding"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"163601\" class=\"elementor elementor-163601\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a2a1fe5 e-flex e-con-boxed e-con\" data-id=\"a2a1fe5\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-731aa7e elementor-widget elementor-widget-text-editor\" data-id=\"731aa7e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h2><strong>In <a href=\"https:\/\/datascientest.com\/de\/nlp-natural-language-processing-eine-einfuhrung\">einem fr\u00fcheren Artikel<\/a> haben wir den Begriff Natural Language Processing (NLP) definiert. In diesem Artikel besch\u00e4ftigen wir uns mit einer der wichtigsten NLP-Methoden, das Verfahren des Word Embedding (dt. Worteinbettung). Word Embedding bezeichnet eine Reihe von Lernmethoden, die darauf abzielen, W\u00f6rter in einem Text durch Vektoren reeller Zahlen zu repr\u00e4sentieren. In diesem Artikel entdeckst Du drei Hauptmethoden, um ein Wort als Vektor darzustellen:<\/strong><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2bf0edc elementor-widget elementor-widget-text-editor\" data-id=\"2bf0edc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li><p>Methode : One Hot Encoding<\/p><\/li><li><p>Methode: Image Embedding<\/p><\/li><li><p>Methode: Word Embedding<\/p><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb266b0 elementor-widget elementor-widget-image\" data-id=\"eb266b0\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"300\" height=\"285\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/Frise.jpg\" class=\"attachment-large size-large wp-image-163604\" alt=\"\" \/>\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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3728e29 e-flex e-con-boxed e-con\" data-id=\"3728e29\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5bb27b8 elementor-widget elementor-widget-heading\" data-id=\"5bb27b8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">One hot encoding <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c0a3b56 elementor-widget elementor-widget-text-editor\" data-id=\"c0a3b56\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">One hot encoding ist die klassische vektorielle Darstellung. Dabei wird jedem Wort des Wortschatzes eine Dimension zugewiesen. Jedes Wort des Wortschatzes wird als bin\u00e4rer Vektor dargestellt, wobei alle seine Werte mit Ausnahme des Wortindex 0 sind.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b631a19 elementor-widget elementor-widget-image\" data-id=\"b631a19\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/athif-shaffy.medium.com\/one-hot-encoding-of-text-b69124bef0a7\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"403\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/1.webp\" class=\"attachment-large size-large wp-image-163608\" alt=\"\" srcset=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/1.webp 768w, https:\/\/datascientest.com\/de\/files\/2022\/09\/1-300x157.webp 300w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\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<div class=\"elementor-element elementor-element-0b45be8 elementor-widget elementor-widget-text-editor\" data-id=\"0b45be8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Nehmen wir diesen Satz als praktisches Beispiel:<\/span><\/p><pre style=\"padding-left: 400px\"><span style=\"font-weight: 400\">\"I think, therefore I am\".<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3a77d36 elementor-widget elementor-widget-text-editor\" data-id=\"3a77d36\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Das Prinzip des One-Hot-Encodding besteht darin, jedem Wort des <\/span><b>Wortschatzes<\/b><span style=\"font-weight: 400\"> einen Index zuzuweisen. Der Wortschatz sieht dann wie ein W\u00f6rterbuch aus:<\/span><\/p><pre style=\"padding-left: 320px\"><span style=\"font-weight: 400\">{'am': 0, 'i': 1, 'therefore': 2, 'think': 3}.<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35b82fb elementor-widget elementor-widget-text-editor\" data-id=\"35b82fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">In diesem Fall <\/span><b>besteht der Wortschatz aus den vier einzigartigen W\u00f6rtern unseres Korpus<\/b><span style=\"font-weight: 400\">. Die Methode besteht darin, das Wort des Wortschatzes als Vektor der Dimension 4 (Wortschatzgr\u00f6\u00dfe) darzustellen. Die Werte des Vektors sind alle 0 au\u00dfer dem Wortindex.<\/span><\/p><pre style=\"padding-left: 440px\"><span style=\"font-weight: 400\">Onehot('i')=0 1 0 0<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d97b00 elementor-widget elementor-widget-text-editor\" data-id=\"5d97b00\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Bei dieser Darstellung haben alle W\u00f6rter <\/span><b>den gleichen Abstand und die gleiche \u00c4hnlichkeit.<\/b><span style=\"font-weight: 400\"> Das Verfahren des <\/span><b>One-Hot-Encoding<\/b><span style=\"font-weight: 400\"> liefert also nur die Information, dass sich ein Wort <\/span><b>von einem anderen unterscheidet<\/b><span style=\"font-weight: 400\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d7c78dd e-flex e-con-boxed e-con\" data-id=\"d7c78dd\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-83d1a94 elementor-widget elementor-widget-heading\" data-id=\"83d1a94\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Image Embedding<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-70c6c54 elementor-widget elementor-widget-text-editor\" data-id=\"70c6c54\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Die Gr\u00f6\u00dfe eines Bildes wird durch seine Pixelzahl definiert. <\/span><b>Konvolutionen<\/b><span style=\"font-weight: 400\"> werden benutzt, um Merkmale zu extrahieren, <\/span><b>die relevanter sind als der Wert der Pixel.<\/b><span style=\"font-weight: 400\"> Beispielsweise k\u00f6nnten die Kerne, die zur Erkennung von Kanten dienen, n\u00fctzlich sein, um bestimmte geometrische Formen zu klassifizieren.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45c51a2 elementor-widget elementor-widget-image\" data-id=\"45c51a2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/towardsdatascience.com\/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"264\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/2-1.webp\" class=\"attachment-large size-large wp-image-163618\" alt=\"\" srcset=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/2-1.webp 768w, https:\/\/datascientest.com\/de\/files\/2022\/09\/2-1-300x103.webp 300w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Convolution Neuronal Network<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64df61f elementor-widget elementor-widget-text-editor\" data-id=\"64df61f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Mit Image embedding werden Convolutions\/Pooling angewendet, um das Bild zu kodieren und <\/span><b>Linearit\u00e4tsbeziehungen zwischen Eigenschaften (Kunststil, spezifische Form &#8230;) und neuen Merkmalen unseres Bildes zu extrahieren.<\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a8fbdbf elementor-widget elementor-widget-image\" data-id=\"a8fbdbf\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.oreilly.com\/library\/view\/practical-deep-learning\/9781492034858\/ch04.html\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"300\" height=\"296\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/3-1.webp\" class=\"attachment-large size-large wp-image-163628\" alt=\"\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Hauptkomponentenanalyse bei Image Embedding<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-607cb05 e-flex e-con-boxed e-con\" data-id=\"607cb05\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-41caa7c elementor-widget elementor-widget-heading\" data-id=\"41caa7c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Word Embedding<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6d6cd0 elementor-widget elementor-widget-text-editor\" data-id=\"d6d6cd0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Genauso kann man <\/span><b>mit Word Embedding durch Verringerung der Dimension den Kontext, die semantische und syntaktische \u00c4hnlichkeit (Genus, Synonyme, &#8230;) eines Wortes erfassen<\/b><span style=\"font-weight: 400\">. Beispielsweise w\u00fcrde man erwarten, dass die W\u00f6rter \u201eHund\u201c und \u201eKatze\u201c durch Vektoren dargestellt werden, die in dem Vektorraum, in dem diese Vektoren definiert sind, relativ wenig voneinander entfernt sind.<\/span><\/p><p><span style=\"font-weight: 400\">Wie bei Bildern m\u00f6chten wir, dass <\/span><b>das Modell die relevantesten Merkmale ausw\u00e4hlt, die das Wort repr\u00e4sentieren<\/b><span style=\"font-weight: 400\">. Zum Beispiel k\u00f6nnte das Merkmal &#8222;Lebewesen&#8220; interessant sein, um &#8222;Hund&#8220; von &#8222;Computer&#8220; zu unterscheiden und &#8222;Hund&#8220; und &#8222;Katze&#8220; einander anzun\u00e4hern.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c0675fd elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"c0675fd\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=sZGuyTLjsco&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-977f90a e-flex e-con-boxed e-con\" data-id=\"977f90a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-104566c elementor-widget elementor-widget-heading\" data-id=\"104566c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Embedding-Matrix<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96134e5 elementor-widget elementor-widget-text-editor\" data-id=\"96134e5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Um die Dimension eines Vektors zu reduzieren, kann man das Ergebnis, das ein Dense Layer zur\u00fcckgibt, als Embedding verwenden, d. h. eine Embedding-Matrix W mit der \u201eOne-Hot\u201c-Darstellung des Wortes multiplizieren:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-956cbc9 elementor-widget elementor-widget-image\" data-id=\"956cbc9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"300\" height=\"146\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/2-3.webp\" class=\"attachment-large size-large wp-image-163638\" alt=\"\" \/>\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<div class=\"elementor-element elementor-element-bcebd08 elementor-widget elementor-widget-text-editor\" data-id=\"bcebd08\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Es gilt :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd39431 elementor-widget elementor-widget-image\" data-id=\"bd39431\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"300\" height=\"51\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/5.webp\" class=\"attachment-large size-large wp-image-163642\" alt=\"\" \/>\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<div class=\"elementor-element elementor-element-f9a2cab elementor-widget elementor-widget-image\" data-id=\"f9a2cab\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/medium.com\/datadriveninvestor\/deep-learning-autoencoders-db265359943e\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"254\" height=\"300\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/4.webp\" class=\"attachment-large size-large wp-image-163646\" alt=\"\" \/>\t\t\t\t\t\t\t\t<\/a>\n\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<div class=\"elementor-element elementor-element-099dc10 elementor-widget elementor-widget-text-editor\" data-id=\"099dc10\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Nachdem wir nun die Methode der Dimensionsreduktion (durch Informationskomprimierung) definiert haben, wie trainieren wir die Embedding-Matrix <\/span><b>W<\/b><span style=\"font-weight: 400\">?<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-13f42de e-flex e-con-boxed e-con\" data-id=\"13f42de\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-61b8971 elementor-widget elementor-widget-heading\" data-id=\"61b8971\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Embedding-Matrix und Klassifikationsproblem<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2634c1e elementor-widget elementor-widget-text-editor\" data-id=\"2634c1e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Um die richtige Embedding-Matrix zu finden kann man sie auf ein \u00fcberwachtes Problem anwenden. Dementsprechend werden wir<\/span><b> die diese Wortdarstellung verwenden, um ein Problem der Sentiment Analysis<\/b><span style=\"font-weight: 400\"> (auch Sentiment Detection) bei IMBD-Rezensionen zu l\u00f6sen. Der Datensatz umfasst 25.000 Filmkritiken (weitere Informationen zum Datensatz findest Du <\/span><a href=\"https:\/\/ai.stanford.edu\/~amaas\/data\/sentiment\/\"><span style=\"font-weight: 400\">hier<\/span><\/a><span style=\"font-weight: 400\">).<\/span><\/p><p><span style=\"font-weight: 400\">Es ist dann m\u00f6glich, die <\/span><b>W-Matrix des word embedding gleichzeitig mit dem Training des Klassifizierungsproblems zu trainieren<\/b><span style=\"font-weight: 400\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ddf2714 elementor-widget elementor-widget-heading\" data-id=\"ddf2714\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Formatierung der Daten:<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7d7dccf elementor-widget elementor-widget-text-editor\" data-id=\"7d7dccf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Um die Daten zu formatieren, muss der Textkorpus in eine Vektorgrafik umgewandelt werden, wobei jedes Review in eine Sequenz von ganzen Zahlen umgewandelt wird (jede ganze Zahl ist der Index eines Wortes):<\/span><\/p><pre style=\"padding-left: 120px\"><span style=\"font-weight: 400\">tokenizer.texts_to_sequences(['hello my dear readers']) -&gt; [[4422, 10, 2974, 6117]]<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f93214 elementor-widget elementor-widget-image\" data-id=\"4f93214\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"586\" src=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/2-2.webp\" class=\"attachment-large size-large wp-image-163656\" alt=\"\" srcset=\"https:\/\/datascientest.com\/de\/files\/2022\/09\/2-2.webp 768w, https:\/\/datascientest.com\/de\/files\/2022\/09\/2-2-300x229.webp 300w\" sizes=\"(max-width: 768px) 100vw, 768px\" \/>\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<div class=\"elementor-element elementor-element-722da18 elementor-widget elementor-widget-text-editor\" data-id=\"722da18\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Unser Modell wird ein einfaches bag of word mit einer Embedding-Methode sein:<\/span><\/p><ol><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Die embedding layer wird jeden Wortindex in einen Embedding-Vektor umwandeln. Die W-Matrix des Embeddings wird gelernt, w\u00e4hrend das Modell trainiert wird. Die resultierenden Dimensionen sind: (Batch, Sequenz, Embedding).<\/span><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Anschlie\u00dfend gibt die GlobalAveragePooling1D layer einen Ausgabevektor mit fester L\u00e4nge f\u00fcr jedes Beispiel zur\u00fcck, indem sie den Durchschnitt \u00fcber die Sequenzdimension bildet. Diese Transformation besteht aus einem bag of word und erm\u00f6glicht es dem Modell, mit Eingaben variabler L\u00e4nge umzugehen.<\/span><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Da wir schlie\u00dflich mit einem Klassifizierungsproblem zu tun haben (positive oder negative Rezension), m\u00fcssen wir Dense-Layer hinzuf\u00fcgen, um das <\/span><i><span style=\"font-weight: 400\">sentiment<\/span><\/i><span style=\"font-weight: 400\"> der Rezension zu klassifizieren.<\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-84b7d91 e-flex e-con-boxed e-con\" data-id=\"84b7d91\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c89e1a7 elementor-widget elementor-widget-heading\" data-id=\"c89e1a7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Und in der Praxis?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff20f45 elementor-widget elementor-widget-shortcode\" data-id=\"ff20f45\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/15boehler\/4437a45f7c2679b319dca43be6dcee1b.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2147958 elementor-widget elementor-widget-text-editor\" data-id=\"2147958\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Auf diese Weise haben wir auf dem Testdatensatz eine Genauigkeit von 0,88 erreicht. Diese Punktzahl ist mit herk\u00f6mmlichen Methoden nur schwer zu erreichen. Au\u00dferdem h\u00e4tten wir auch ein <\/span><b>Tf-idf-Ma\u00df<\/b><span style=\"font-weight: 400\"> (anstelle von bag of word) verwenden k\u00f6nnen, um unsere Sequenz in einen Vektor umzuwandeln.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-78b73d6 e-flex e-con-boxed e-con\" data-id=\"78b73d6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8c21e5a elementor-widget elementor-widget-heading\" data-id=\"8c21e5a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Schlussfolgerungen:<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b70dd99 elementor-widget elementor-widget-text-editor\" data-id=\"b70dd99\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">So ein Modell liefert in der Regel bessere Ergebnisse als herk\u00f6mmliche Ans\u00e4tze. Es erm\u00f6glicht auch, die Dimension des Problems und damit die Lernaufgabe zu reduzieren. , Da wir nur eine Information \u00fcber das Gef\u00fchl geben, erfasst jedoch das word embedding nur schwerlich eine andere Beziehung als diese.<\/span><\/p><p><span style=\"font-weight: 400\">In einem n\u00e4chsten Artikel werden wir die embedding-Matrix unbeaufsichtigt mithilfe des bekannten word2vec-Algorithmus trainieren. Da das Training un\u00fcberwacht erfolgt, werden wir die Verzerrung vermeiden, die mit der L\u00f6sung eines \u00fcberwachten Problems verbunden ist.<\/span><\/p><p><span style=\"font-weight: 400\">Hat Dir dieser Artikel gefallen? M\u00f6chtest Du noch mehr \u00fcber Data Science lernen?<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d63bb56 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"d63bb56\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/de\/unsere-aus-und-weiterbildungen\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Starte jetzt eines unserer Trainings!<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>In einem fr\u00fcheren Artikel haben wir den Begriff Natural Language Processing (NLP) definiert. In diesem Artikel besch\u00e4ftigen wir uns mit einer der wichtigsten NLP-Methoden, das Verfahren des Word Embedding (dt. Worteinbettung). Word Embedding bezeichnet eine Reihe von Lernmethoden, die darauf abzielen, W\u00f6rter in einem Text durch Vektoren reeller Zahlen zu repr\u00e4sentieren. In diesem Artikel entdeckst [&hellip;]<\/p>\n","protected":false},"author":47,"featured_media":163602,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2454],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Das Word Embedding | DataScientest<\/title>\n<meta name=\"description\" content=\"Lernen Sie das Word Embedding kennen, eine der wichtigsten Methoden, die f\u00fcr NLP- Natural Language Processing verwendet werden.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/de\/word-embedding\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Das Word Embedding\" \/>\n<meta property=\"og:description\" content=\"In einem fr\u00fcheren Artikel haben wir den Begriff Natural Language Processing (NLP) definiert. In diesem Artikel besch\u00e4ftigen wir uns mit einer der\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/de\/word-embedding\" \/>\n<meta property=\"og:site_name\" content=\"Weiterbildung Data Science | DataScientest.com\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2022-09-13T16:56:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-23T16:19:25+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/de\/wp-content\/blogs.dir\/8\/files\/2022\/09\/illu_wordembedding_1520x600_Plan-de-travail-1.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"768\" \/>\n\t<meta property=\"og:image:height\" content=\"303\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Adriano R\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"Adriano R\" \/>\n\t<meta name=\"twitter:label2\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/de\/word-embedding#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/de\/word-embedding\"},\"author\":{\"name\":\"Adriano R\",\"@id\":\"https:\/\/datascientest.com\/de\/#\/schema\/person\/3d2560836c47019eb12b7b77f3a15378\"},\"headline\":\"Das Word Embedding\",\"datePublished\":\"2022-09-13T16:56:12+00:00\",\"dateModified\":\"2024-01-23T16:19:25+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/de\/word-embedding\"},\"wordCount\":829,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/de\/#organization\"},\"articleSection\":[\"Data Science\"],\"inLanguage\":\"de\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/de\/word-embedding#respond\"]}],\"copyrightYear\":\"2022\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/de\/word-embedding\",\"url\":\"https:\/\/datascientest.com\/de\/word-embedding\",\"name\":\"Das Word Embedding | DataScientest\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/de\/#website\"},\"datePublished\":\"2022-09-13T16:56:12+00:00\",\"dateModified\":\"2024-01-23T16:19:25+00:00\",\"description\":\"Lernen Sie das Word Embedding kennen, eine der wichtigsten Methoden, die f\u00fcr NLP- Natural Language Processing verwendet werden.\",\"inLanguage\":\"de\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/de\/word-embedding\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/de\/#website\",\"url\":\"https:\/\/datascientest.com\/de\/\",\"name\":\"Weiterbildung Data Science | DataScientest.com\",\"description\":\"F\u00fchrender Anbieter von Schulungen im Bereich Data Science\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/de\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/de\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"de\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/de\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/de\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"de\",\"@id\":\"https:\/\/datascientest.com\/de\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/de\/wp-content\/blogs.dir\/8\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/de\/wp-content\/blogs.dir\/8\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/de\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/company\/datascientest-germany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/de\/#\/schema\/person\/3d2560836c47019eb12b7b77f3a15378\",\"name\":\"Adriano R\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"de\",\"@id\":\"https:\/\/datascientest.com\/de\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/fbfbc3a7811c63caf3333396a123058f?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/fbfbc3a7811c63caf3333396a123058f?s=96&d=mm&r=g\",\"caption\":\"Adriano R\"},\"sameAs\":[\"user\"],\"url\":\"https:\/\/datascientest.com\/de\/author\/adriano-r\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Das Word Embedding | DataScientest","description":"Lernen Sie das Word Embedding kennen, eine der wichtigsten Methoden, die f\u00fcr NLP- Natural Language Processing verwendet werden.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/de\/word-embedding","og_locale":"de_DE","og_type":"article","og_title":"Das Word Embedding","og_description":"In einem fr\u00fcheren Artikel haben wir den Begriff Natural Language Processing (NLP) definiert. In diesem Artikel besch\u00e4ftigen wir uns mit einer der","og_url":"https:\/\/datascientest.com\/de\/word-embedding","og_site_name":"Weiterbildung Data Science | DataScientest.com","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2022-09-13T16:56:12+00:00","article_modified_time":"2024-01-23T16:19:25+00:00","og_image":[{"width":768,"height":303,"url":"https:\/\/datascientest.com\/de\/wp-content\/blogs.dir\/8\/files\/2022\/09\/illu_wordembedding_1520x600_Plan-de-travail-1.webp","type":"image\/webp"}],"author":"Adriano R","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Verfasst von":"Adriano R","Gesch\u00e4tzte Lesezeit":"7 Minuten"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/de\/word-embedding#article","isPartOf":{"@id":"https:\/\/datascientest.com\/de\/word-embedding"},"author":{"name":"Adriano R","@id":"https:\/\/datascientest.com\/de\/#\/schema\/person\/3d2560836c47019eb12b7b77f3a15378"},"headline":"Das Word Embedding","datePublished":"2022-09-13T16:56:12+00:00","dateModified":"2024-01-23T16:19:25+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/de\/word-embedding"},"wordCount":829,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/de\/#organization"},"articleSection":["Data Science"],"inLanguage":"de","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/de\/word-embedding#respond"]}],"copyrightYear":"2022","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/de\/word-embedding","url":"https:\/\/datascientest.com\/de\/word-embedding","name":"Das Word Embedding | DataScientest","isPartOf":{"@id":"https:\/\/datascientest.com\/de\/#website"},"datePublished":"2022-09-13T16:56:12+00:00","dateModified":"2024-01-23T16:19:25+00:00","description":"Lernen Sie das Word Embedding kennen, eine der wichtigsten Methoden, die f\u00fcr NLP- Natural Language Processing verwendet werden.","inLanguage":"de","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/de\/word-embedding"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/de\/#website","url":"https:\/\/datascientest.com\/de\/","name":"Weiterbildung Data Science | DataScientest.com","description":"F\u00fchrender Anbieter von Schulungen im Bereich Data Science","publisher":{"@id":"https:\/\/datascientest.com\/de\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/de\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"de"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/de\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/de\/","logo":{"@type":"ImageObject","inLanguage":"de","@id":"https:\/\/datascientest.com\/de\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/de\/wp-content\/blogs.dir\/8\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/de\/wp-content\/blogs.dir\/8\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/de\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/company\/datascientest-germany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/de\/#\/schema\/person\/3d2560836c47019eb12b7b77f3a15378","name":"Adriano R","image":{"@type":"ImageObject","inLanguage":"de","@id":"https:\/\/datascientest.com\/de\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/fbfbc3a7811c63caf3333396a123058f?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/fbfbc3a7811c63caf3333396a123058f?s=96&d=mm&r=g","caption":"Adriano R"},"sameAs":["user"],"url":"https:\/\/datascientest.com\/de\/author\/adriano-r"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/posts\/163601"}],"collection":[{"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/users\/47"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/comments?post=163601"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/posts\/163601\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/media\/163602"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/media?parent=163601"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/categories?post=163601"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/de\/wp-json\/wp\/v2\/tags?post=163601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}