QueryIntent-Entity-NER: Query Intent and Entity Extraction for SEO

Type: Academic | Domain: SEO, NLP
Hugging Face: syeedalireza/query-intent-entity-ner

Multi-task: query intent classification and named-entity extraction for search content planning.

Author

Alireza Aminzadeh

Problem

Understanding intent (informational, navigational, transactional) and key entities in queries improves content and keyword strategy.

Approach

Intent: Multi-class (e.g. informational / navigational / transactional / commercial).
Entities: NER (ORG, PRODUCT, LOC, etc.) from query text.
Models: Hugging Face transformer (e.g. BERT) for sequence classification + token classification, or pipeline with spaCy/transformers.

Tech Stack

Category	Tools
NLP	Hugging Face Transformers, tokenizers
NER	spaCy (optional), transformers NER head
ML	PyTorch, scikit-learn
Data	pandas, NumPy

Setup

pip install -r requirements.txt

Usage

python train.py
python inference.py --query "best running shoes for flat feet"

Project structure

03_query-intent-entity-ner/
├── config.py
├── train.py           # BERT (or HF) sequence classification
├── inference.py       # Single query or batch CSV; CPU/GPU auto
├── requirements.txt
├── .env.example
├── data/
│   └── query_intent.csv   # Sample: query, intent
└── models/

Data

Sample data (included): data/query_intent.csv — columns: query, intent.
Intent labels: informational, navigational, transactional, commercial.
Set DATA_PATH in .env if using another file.

License

MIT.

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query-intent-entity-ner