Natural Language Processing
Implement NLP features including text classification, named entity recognition, sentiment analysis, keyword extraction, language detection, and semantic search using transformer models.
Project Milestone & Feature Breakdown
1 NLP Infrastructure
Set up NLP models and pipeline
5 pts 1 week 2 Features
NLP Infrastructure
Set up NLP models and pipeline
Model Selection
Choose and deploy NLP models (BERT, spaCy)
Text Preprocessing
Tokenization, normalization, cleaning
Deliverables
- NLP models
- Preprocessing pipeline
- Inference API
2 NLP Features Implementation
Implement core NLP capabilities
8 pts 1-2 weeks 3 Features
NLP Features Implementation
Implement core NLP capabilities
Text Classification
Categorize text into predefined classes
Entity Extraction
Extract names, dates, locations, etc.
Sentiment Analysis
Determine sentiment (positive/negative/neutral)
Deliverables
- Classification API
- Entity extraction
- Sentiment analysis
3 Advanced NLP Features
Semantic search and advanced features
5 pts 1 week 2 Features
Advanced NLP Features
Semantic search and advanced features
Semantic Search
Vector-based semantic similarity search
Keyword Extraction
Extract important keywords and phrases
Deliverables
- Semantic search
- Keyword extraction
- Embeddings
Technical Stack
Key Considerations
Model size and performance
Language support
Accuracy vs speed tradeoff
Fine-tuning requirements
Deployment infrastructure
Success Criteria
High accuracy on target domain
Low latency inference
Supports multiple languages
Models updated regularly
API well-documented
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