content-tagging 开源项目推荐（12 个）

dbpedia-spotlight /dbpedia-spotlight

DBpedia Spotlight 是一款用于自动注释文本中提及 DBpedia 资源的工具。

10up /classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence.

akashdathan /opencalais-tagging

Open Calais attaches intelligent metadata-tags to your unstructured content, enabling powerful text analytics. The Open Calais natural language processing engine automatically analyzes and tags your input files in such a way that your consuming application can both easily pinpoint relevant data, and effectively leverage the invaluable intelligen…

focusreactive /headless-cms-gpt-sdk

AI SDK for headless content, with Sanity and Storyblok plugins that translate, summarize and tag documents from inside the CMS while preserving structure.

whuu /Sc.CustomTagger

Sitecore Content Tagging with Cortex and Custom Tags Structure

chigwell /vrscene-parser

A new package would process textual descriptions of virtual reality scenes or environments and return structured, validated outputs that describe the scene in a standardized format. It would use an LL

xsukax /xsukax-Flat-File-CMS

xsukax Flat-File CMS combines enterprise-grade security with flexible theming in a lightweight PHP-based CMS. Features external password storage and dynamic theme switching without any database dependencies.

redswimmer /netflix-genre-prediction

Multi-label text classification for predicting Netflix content genres

syarifanur /vrscene-parser

🌐 Streamline VR scene creation by converting natural-language descriptions into structured formats with vrscene-parser.

SMcCandlish /phpBB-ext-RHTopicTags

phpBB 3.3 extension for tagging topics with keywords.

swilliams9772 /multimodal-content-tagging

Automatically assign fine-grained editorial & merchandising tags to video scenes using multimodal AI — video frames, audio, subtitles, OCR, and metadata

everyinfra /analysis-skills

Free analysis Skills for Codex and Claude Code — four domain skills (ecommerce comparison, social content performance, content tagging, review insights) plus a shared analysis-foundation discipline layer: numbers from code, denominators named, uncertainty reported. Use your own model.