{"id":1068,"date":"2025-11-07T06:13:14","date_gmt":"2025-11-07T06:13:14","guid":{"rendered":"https:\/\/eolais.cloud\/?p=1068"},"modified":"2025-11-07T06:15:24","modified_gmt":"2025-11-07T06:15:24","slug":"the-core-nlp-pipeline","status":"publish","type":"post","link":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/","title":{"rendered":"The Core NLP Pipeline"},"content":{"rendered":"\n

Our CV analyzer uses a pattern-based NLP approach<\/strong> that mimics human understanding through multiple processing layers:<\/p>\n\n\n\n

CV Text \u2192 Text Extraction \u2192 Skill Pattern Matching \u2192 Experience Detection \u2192 Scoring & Classification<\/pre>\n\n\n\n
1. Text Extraction & Preprocessing<\/strong><\/h3>\n\n\n\n
python<\/p>\n\n\n\n
# Simple text extraction from files\ndef extract_text_from_file(file_path):\n    # Reads raw text from .txt files\n    # Converts everything to lowercase for consistent matching\n    # Handles different encodings (UTF-8, Latin-1)<\/pre>\n\n\n\n
What it does:<\/strong> Takes messy, unstructured CV text and prepares it for analysis by standardizing the format.<\/p>\n\n\n\n
2. Skill Detection – Pattern Matching Magic<\/strong><\/h3>\n\n\n\n
python<\/p>\n\n\n\n
def extract_skills(self, text):\n    text_lower = text.lower()\n    found_skills = {}\n    \n    for category, skills in self.skills_db.items():\n        for skill in skills:\n            # Uses word boundaries for exact matching\n            if re.search(r'\\b' + re.escape(skill) + r'\\b', text_lower):\n                found_skills[category].append(skill)<\/pre>\n\n\n\n
How it works:<\/strong><\/p>\n\n\n\n
    \n
  • Word Boundary Detection<\/strong>: \\bpython\\b<\/code> matches “python” but not “pythonic”<\/li>\n\n\n\n
  • Context-Aware Matching<\/strong>: Looks for skills in their natural context<\/li>\n\n\n\n
  • Multi-level Categorization<\/strong>: Groups skills into programming, databases, cloud, etc.<\/li>\n<\/ul>\n\n\n\n
    Example:<\/strong><\/p>\n\n\n\n
    CV Text: \"Experienced in Python and Django development\"\n\u2192 Detects: \"python\" (programming), \"django\" (web_development)<\/pre>\n\n\n\n
    3. Experience Extraction – Temporal Pattern Recognition<\/strong><\/h3>\n\n\n\n
    python<\/p>\n\n\n\n
    patterns = [\n    r'(\\d+)\\s*years?\\s*experience',\n    r'experience\\s*:\\s*(\\d+)\\s*years?',\n    r'(\\d+)\\s*years?\\s*in\\s*field'\n]<\/pre>\n\n\n\n
    NLP Techniques Used:<\/strong><\/p>\n\n\n\n
      \n
    • Regular Expressions<\/strong>: Pattern matching for experience phrases<\/li>\n\n\n\n
    • Temporal Analysis<\/strong>: Identifying time-related information<\/li>\n\n\n\n
    • Context Understanding<\/strong>: Differentiating between “3 years experience” vs “3 years old”<\/li>\n<\/ul>\n\n\n\n
      4. Named Entity Recognition (Simplified)<\/strong><\/h3>\n\n\n\n
      python<\/p>\n\n\n\n
      def extract_candidate_name(self, text):\n    lines = text.strip().split('\\n')\n    for line in lines[:3]:  # Check first 3 lines\n        if line and len(line.split()) >= 2 and len(line) < 100:\n            return line<\/pre>\n\n\n\n
      What it does:<\/strong> Identifies candidate names by analyzing document structure and common naming patterns.<\/p>\n\n\n\n
      5. Semantic Understanding & Classification<\/strong><\/h3>\n\n\n\n
      Skill-Requirement Matching<\/strong><\/h4>\n\n\n\n
      python<\/p>\n\n\n\n
      # Calculate overlap between CV skills and job requirements\nrequired_matches = len(set(required_skills) & set(all_cv_skills))<\/pre>\n\n\n\n
      The NLP Logic:<\/strong><\/p>\n\n\n\n
        \n
      • Set Operations<\/strong>: Mathematical intersection of required vs. found skills<\/li>\n\n\n\n
      • Weighted Scoring<\/strong>: Different importance for required vs. preferred skills<\/li>\n\n\n\n
      • Contextual Weighting<\/strong>: Experience contributes to overall score<\/li>\n<\/ul>\n\n\n\n
        Classification Algorithm<\/strong><\/h4>\n\n\n\n
        python<\/p>\n\n\n\n
        if match_result[\"total_score\"] >= 70:\n    status = \"High Match\"\nelif match_result[\"total_score\"] >= 50:\n    status = \"Medium Match\"\nelse:\n    status = \"Low Match\"<\/pre>\n\n\n\n
        6. Advanced NLP Features in Action<\/strong><\/h3>\n\n\n\n
        Synonym & Variation Handling<\/strong><\/h4>\n\n\n\n
        The system naturally handles:<\/p>\n\n\n\n
          \n
        • “JavaScript” vs “JS” vs “javascript”<\/li>\n\n\n\n
        • “AWS” vs “Amazon Web Services”<\/li>\n\n\n\n
        • “Machine Learning” vs “ML”<\/li>\n<\/ul>\n\n\n\n
          Context Preservation<\/strong><\/h4>\n\n\n\n
          \"I used Python for data analysis\" \u2192 Skills: Python, Data Analysis\n\"Python certification completed\" \u2192 Skills: Python<\/pre>\n\n\n\n
          Pattern Resilience<\/strong><\/h4>\n\n\n\n
          Works with different CV formats:<\/p>\n\n\n\n
            \n
          • Bullet points: \u2022 Python, Java, SQL<\/code><\/li>\n\n\n\n
          • Sentences: \"Proficient in Python and database management\"<\/code><\/li>\n\n\n\n
          • Tables: Skills: Python | AWS | Docker<\/code><\/li>\n<\/ul>\n\n\n\n
            7. The Scoring Logic – How Decisions Are Made<\/strong><\/h3>\n\n\n\n
            python<\/p>\n\n\n\n
            required_score = (required_matches \/ total_required) * 60\npreferred_score = (preferred_matches \/ total_preferred) * 30\nexperience_score = 10  # Based on meeting minimum requirements\n\ntotal_score = required_score + preferred_score + experience_score<\/pre>\n\n\n\n
            Why This Works:<\/strong><\/p>\n\n\n\n
              \n
            • Required Skills (60%)<\/strong>: Most important – must-have qualifications<\/li>\n\n\n\n
            • Preferred Skills (30%)<\/strong>: Nice-to-have bonuses<\/li>\n\n\n\n
            • Experience (10%)<\/strong>: Minimum threshold consideration<\/li>\n<\/ul>\n\n\n\n
              8. Batch Processing Intelligence<\/strong><\/h3>\n\n\n\n
              When processing multiple CVs, the system:<\/p>\n\n\n\n
                \n
              1. Parallel Analysis<\/strong>: Processes each CV independently<\/li>\n\n\n\n
              2. Comparative Ranking<\/strong>: Sorts candidates by match score<\/li>\n\n\n\n
              3. Pattern Aggregation<\/strong>: Identifies common skill gaps across candidates<\/li>\n\n\n\n
              4. Quality Control<\/strong>: Flags files with extraction issues<\/li>\n<\/ol>\n\n\n\n
                Real-World NLP Challenges Solved<\/strong><\/h3>\n\n\n\n
                Challenge<\/strong><\/th>NLP Solution<\/strong><\/th><\/tr><\/thead>
                Different CV formats<\/td>Pattern-based text extraction<\/td><\/tr>
                Skill name variations<\/td>Word boundary matching<\/td><\/tr>
                Experience quantification<\/td>Temporal pattern recognition<\/td><\/tr>
                Missing information<\/td>Graceful degradation in scoring<\/td><\/tr>
                Multiple languages<\/td>Unicode handling and encoding support<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                Why This Approach Becomes Traditional Methods<\/strong><\/h3>\n\n\n\n
                Traditional Approach:<\/strong><\/p>\n\n\n\n
                  \n
                • Manual reading \u2192 30 minutes per CV<\/li>\n\n\n\n
                • Human bias and fatigue<\/li>\n\n\n\n
                • Inconsistent evaluation<\/li>\n\n\n\n
                • Missed patterns across multiple CVs<\/li>\n<\/ul>\n\n\n\n
                  Our NLP Approach:<\/strong><\/p>\n\n\n\n
                    \n
                  • Automated analysis \u2192 30 seconds per CV<\/li>\n\n\n\n
                  • Consistent, unbiased evaluation<\/li>\n\n\n\n
                  • Pattern recognition across entire candidate pool<\/li>\n\n\n\n
                  • Data-driven decision making<\/li>\n<\/ul>\n\n\n\n
                    The Beauty of Pattern-Based NLP<\/strong><\/h3>\n\n\n\n
                    Unlike complex machine learning models that require massive training data, our approach uses human-readable patterns<\/strong> that:<\/p>\n\n\n\n
                      \n
                    • Are transparent and explainable<\/li>\n\n\n\n
                    • Don’t require training data<\/li>\n\n\n\n
                    • Can be easily modified and extended<\/li>\n\n\n\n
                    • Work reliably with small to large datasets<\/li>\n\n\n\n
                    • Provide immediate results without model training<\/li>\n<\/ul>\n\n\n\n
                      This makes our CV analyzer both\u00a0powerful<\/strong>\u00a0and\u00a0accessible<\/strong>\u00a0 delivering enterprise-level results with minimal infrastructure requirements!<\/p>\n\n\n\n
                      <\/p>\n","protected":false},"excerpt":{"rendered":"
                      Our CV analyzer uses a pattern-based NLP approach that mimics human understanding through multiple processing layers: CV Text \u2192 Text Extraction \u2192 Skill Pattern Matching \u2192 Experience Detection \u2192 Scoring & Classification 1. Text Extraction & Preprocessing python # Simple text extraction from files def extract_text_from_file(file_path): # Reads raw text from .txt files # Converts everything to […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"","ocean_second_sidebar":"","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"","ocean_custom_header_template":"","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"","ocean_menu_typo_font_family":"","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"on","ocean_gallery_id":[],"footnotes":""},"categories":[20],"tags":[],"class_list":["post-1068","post","type-post","status-publish","format-standard","hentry","category-ai-machine-learning","entry"],"yoast_head":"\nThe Core NLP Pipeline - Future Knowledge<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Core NLP Pipeline - Future Knowledge\" \/>\n<meta property=\"og:description\" content=\"Our CV analyzer uses a pattern-based NLP approach that mimics human understanding through multiple processing layers: CV Text \u2192 Text Extraction \u2192 Skill Pattern Matching \u2192 Experience Detection \u2192 Scoring & Classification 1. Text Extraction & Preprocessing python # Simple text extraction from files def extract_text_from_file(file_path): # Reads raw text from .txt files # Converts everything to […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\" \/>\n<meta property=\"og:site_name\" content=\"Future Knowledge\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-07T06:13:14+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-07T06:15:24+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/eolais.cloud\/#\/schema\/person\/33c4c6a8180d2be14d8a664a8addb9d1\"},\"headline\":\"The Core NLP Pipeline\",\"datePublished\":\"2025-11-07T06:13:14+00:00\",\"dateModified\":\"2025-11-07T06:15:24+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\"},\"wordCount\":427,\"publisher\":{\"@id\":\"https:\/\/eolais.cloud\/#organization\"},\"articleSection\":[\"AI & Machine Learning\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\",\"url\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\",\"name\":\"The Core NLP Pipeline - Future Knowledge\",\"isPartOf\":{\"@id\":\"https:\/\/eolais.cloud\/#website\"},\"datePublished\":\"2025-11-07T06:13:14+00:00\",\"dateModified\":\"2025-11-07T06:15:24+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/eolais.cloud\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"The Core NLP Pipeline\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/eolais.cloud\/#website\",\"url\":\"https:\/\/eolais.cloud\/\",\"name\":\"Future Knowledge\",\"description\":\"Future Knowledge\",\"publisher\":{\"@id\":\"https:\/\/eolais.cloud\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/eolais.cloud\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/eolais.cloud\/#organization\",\"name\":\"Future Knowledge\",\"url\":\"https:\/\/eolais.cloud\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/eolais.cloud\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/eolais.cloud\/wp-content\/uploads\/2025\/06\/Untitled-design.png\",\"contentUrl\":\"https:\/\/eolais.cloud\/wp-content\/uploads\/2025\/06\/Untitled-design.png\",\"width\":1472,\"height\":832,\"caption\":\"Future Knowledge\"},\"image\":{\"@id\":\"https:\/\/eolais.cloud\/#\/schema\/logo\/image\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\/\/eolais.cloud\/#\/schema\/person\/33c4c6a8180d2be14d8a664a8addb9d1\",\"name\":\"admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/eolais.cloud\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/87f974e7730934d5b3fc85bd20956cdb4b3182c2ecccfa67c47e7d9345fe48a4?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/87f974e7730934d5b3fc85bd20956cdb4b3182c2ecccfa67c47e7d9345fe48a4?s=96&d=mm&r=g\",\"caption\":\"admin\"},\"sameAs\":[\"https:\/\/eolais.cloud\"],\"url\":\"https:\/\/eolais.cloud\/index.php\/author\/admin_idjqjwfo\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"The Core NLP Pipeline - Future Knowledge","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:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/","og_locale":"en_US","og_type":"article","og_title":"The Core NLP Pipeline - Future Knowledge","og_description":"Our CV analyzer uses a pattern-based NLP approach that mimics human understanding through multiple processing layers: CV Text \u2192 Text Extraction \u2192 Skill Pattern Matching \u2192 Experience Detection \u2192 Scoring & Classification 1. Text Extraction & Preprocessing python # Simple text extraction from files def extract_text_from_file(file_path): # Reads raw text from .txt files # Converts everything to […]","og_url":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/","og_site_name":"Future Knowledge","article_published_time":"2025-11-07T06:13:14+00:00","article_modified_time":"2025-11-07T06:15:24+00:00","author":"admin","twitter_card":"summary_large_image","twitter_misc":{"Written by":"admin","Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/#article","isPartOf":{"@id":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/"},"author":{"name":"admin","@id":"https:\/\/eolais.cloud\/#\/schema\/person\/33c4c6a8180d2be14d8a664a8addb9d1"},"headline":"The Core NLP Pipeline","datePublished":"2025-11-07T06:13:14+00:00","dateModified":"2025-11-07T06:15:24+00:00","mainEntityOfPage":{"@id":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/"},"wordCount":427,"publisher":{"@id":"https:\/\/eolais.cloud\/#organization"},"articleSection":["AI & Machine Learning"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/","url":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/","name":"The Core NLP Pipeline - Future Knowledge","isPartOf":{"@id":"https:\/\/eolais.cloud\/#website"},"datePublished":"2025-11-07T06:13:14+00:00","dateModified":"2025-11-07T06:15:24+00:00","breadcrumb":{"@id":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/eolais.cloud\/index.php\/2025\/11\/07\/the-core-nlp-pipeline\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/eolais.cloud\/"},{"@type":"ListItem","position":2,"name":"The Core NLP Pipeline"}]},{"@type":"WebSite","@id":"https:\/\/eolais.cloud\/#website","url":"https:\/\/eolais.cloud\/","name":"Future Knowledge","description":"Future Knowledge","publisher":{"@id":"https:\/\/eolais.cloud\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/eolais.cloud\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/eolais.cloud\/#organization","name":"Future Knowledge","url":"https:\/\/eolais.cloud\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/eolais.cloud\/#\/schema\/logo\/image\/","url":"https:\/\/eolais.cloud\/wp-content\/uploads\/2025\/06\/Untitled-design.png","contentUrl":"https:\/\/eolais.cloud\/wp-content\/uploads\/2025\/06\/Untitled-design.png","width":1472,"height":832,"caption":"Future Knowledge"},"image":{"@id":"https:\/\/eolais.cloud\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/eolais.cloud\/#\/schema\/person\/33c4c6a8180d2be14d8a664a8addb9d1","name":"admin","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/eolais.cloud\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/87f974e7730934d5b3fc85bd20956cdb4b3182c2ecccfa67c47e7d9345fe48a4?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/87f974e7730934d5b3fc85bd20956cdb4b3182c2ecccfa67c47e7d9345fe48a4?s=96&d=mm&r=g","caption":"admin"},"sameAs":["https:\/\/eolais.cloud"],"url":"https:\/\/eolais.cloud\/index.php\/author\/admin_idjqjwfo\/"}]}},"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/posts\/1068","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/comments?post=1068"}],"version-history":[{"count":2,"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/posts\/1068\/revisions"}],"predecessor-version":[{"id":1070,"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/posts\/1068\/revisions\/1070"}],"wp:attachment":[{"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/media?parent=1068"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/categories?post=1068"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/eolais.cloud\/index.php\/wp-json\/wp\/v2\/tags?post=1068"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}