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natural-language-understanding

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This project uses BERT to build a QA system fine-tuned on the SQuAD dataset, improving the accuracy and efficiency of question-answering tasks. We address challenges in contextual understanding and ambiguity handling to enhance user experience and system performance.

  • Updated Jun 1, 2024
  • Jupyter Notebook

Successfully developed a Named Entity Recognition (NER) model for German text using a Bidirectional LSTM with Attention on the Multilingual NER dataset, effectively identifying entities across multilingual corpora with contextual understanding.

  • Updated Jul 4, 2025
  • Jupyter Notebook

AI-powered Healthcare Assistant Chatbot built with Azure Cognitive Services Language Studio Custom Question Answering (Custom QA). Features 8-domain knowledge base, 101 Q&A pairs, multi-turn follow-up prompts, chit-chat integration, Azure Bot Service deployment, and live Web Chat widget hosted on Netlify.

  • Updated Apr 28, 2026
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Clarabridge — independent third-party profile of a public API surface, by API Evangelist. Clarabridge was a customer experience management (CEM) and conversational analytics company known for its natural-language-understanding platform that analyzed unstructured customer feedback — contact-center transcripts, chat, email, surveys, social media, and

  • Updated Oct 24, 2026

Successfully implemented a Named Entity Recognition (NER) model on the WNUT 2016 dataset using Stacked BiLSTMs with Attention to effectively capture contextual dependencies and improve entity tagging accuracy in noisy user-generated text.

  • Updated Jul 4, 2025
  • Jupyter Notebook

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