Semantic Search Engine
Semantic Search Engine for News Articles Using Sentence Embeddings
Client
Self Development
YeAr
2025
Category
Natural Language Processing (NLP)
Service
Automation & Efficiency Gains
Tools / Languages Used
- Python
- sentence-transformers (for embeddings)
- FAISS or Chroma (for efficient similarity search)
- pandas, numpy (data handling)
- streamlit or gradio (for interactive demo)
- Optional: Hugging Face Datasets API for loading text corpora
Technical Skills
- Text embedding generation using pretrained transformer models (Sentence-BERT)
- Vector similarity search (cosine similarity, FAISS index)
- Data preprocessing and normalization
- Building scalable text retrieval systems
- Model evaluation through relevance ranking metrics (precision@k, recall@k)
- Deployment of interactive NLP applications
Soft Skills
- Problem framing: Defined “relevance” and designed evaluation to measure it.
- Systems thinking: Architected a modular retrieval system separating embedding, storage, and query layers.
- Communication: Simplified complex NLP concepts (embeddings, vector similarity) for non-technical audiences.
- User experience: Designed a clean, intuitive search interface for demonstrations.
Step 1: Exploratory Data ANalysis
- Collected ~10,000+ news articles from a public dataset (e.g., AG News or BBC News).
- Inspected categories, word counts, and content diversity.
- Cleaned and normalized text (removed HTML, punctuation, and stopwords).
- Sample insights:
- Articles from “Technology” and “Business” often overlapped semantically (shared topics like startups, AI, innovation).
- Keyword-based search struggled with contextual matches (e.g., “AI chip” vs “semiconductor company”).
Step 2: Solution Design
- Used Sentence-BERT (all-MiniLM-L6-v2) to create 384-dimensional sentence embeddings for each article.
- Stored embeddings in a FAISS index for efficient nearest-neighbor search.
- For a user query:
- Encode the query into an embedding.
- Compute cosine similarity with all article vectors.
- Return the top-k most semantically similar results.
- Compared performance with simple TF-IDF cosine similarity to show the power of embeddings.
Step 3: Model Assessment
- Defined evaluation metric: Precision@k — proportion of retrieved articles that share the correct category with the query.
- Compared:
- TF-IDF similarity: 0.58 precision@5
- Sentence-BERT similarity: 0.82 precision@5
- Qualitative analysis showed the embedding model retrieved conceptually relevant articles even with different phrasing (e.g., query “spacecraft launch” matched “NASA delays rocket mission”).
Step 4: Results / How It’s Used
Built the model for self development. No further deployments associated.
Built the model for self development. No further deployments associated.
