Sentence-Based Segmentation

Sentence-based segmentation creates chunks containing complete sentences, preserving the smallest coherent units of thought while controlling chunk size. This approach balances semantic integrity with size consistency:

Core Mechanism: Text is first split into individual sentences using natural language processing techniques (punctuation rules, language models, etc.). These sentences are then grouped into chunks up to a maximum size threshold, ensuring no sentence is split mid-way.

Advantages:

• Semantic Preservation: Maintains complete thoughts as expressed in sentences
• Flexible Grouping: Can combine related sentences while respecting maximum size limits
• Language Awareness: Properly handles various sentence structures and punctuation patterns

Disadvantages:

• Context Limitations: May separate closely related sentences across chunks
• Processing Overhead: Requires more sophisticated text analysis than simpler methods
• Inconsistency with Complex Sentences: Very long sentences can still create challenges

Fixed-Size with Overlap Enhancement:

Many implementations add overlapping content between adjacent chunks (typically 10-20% of chunk size). This technique helps maintain context across chunk boundaries by including the end of the previous chunk at the beginning of the next one. Overlapping is particularly valuable for sentence and paragraph-based approaches as it helps preserve the flow of ideas that might span chunk boundaries.

Best Use Cases: Sentence-based segmentation excels for question-answering applications where complete sentences provide important context. It's also effective for content with complex ideas developed across multiple short sentences, where preserving sentence integrity is more important than paragraph structure.