How to Chunk Text Data – A Comparative Analysis
Two categories of chunking methods: rule-based (explicit separators) and semantic clustering (inherent text meaning).
Rule-Based Methods
NLTK Sentence Tokenizer
sent_tokenize() → 2,670 sentences avg. 78 chars. Language-dependent, struggles with abbreviations, no semantic understanding between sentences.
spaCy Sentence Splitter
Linguistic rules → 2,336 sentences avg. 89 chars. Smaller chunks adhering strictly to sentence boundaries.
LangChain Recursive Character Text Splitter
Splits at ["\n\n", "\n", " ", ""] with configurable chunk size and overlap. Default: 3,205 chunks, 65.8 chars avg. Custom (size=300, overlap=30): 1,404 chunks.
Uniform distribution; useful for standardized downstream processing.
Semantic Clustering Methods
KMeans Clustering
Uses sentence-transformers embeddings + scikit-learn KMeans. Groups semantically similar sentences.
Limitation: loses original sentence order, computationally intensive, not real-time friendly.
Adjacent Sentence Clustering
Clusters consecutive sentences by cosine similarity threshold. Preserves sentence order.
Process:
- Normalize sentence embeddings
- Form clusters when similarity < threshold
- Apply length checks (60–3000 chars)
- Recursively recluster oversized groups
Balances context preservation with efficiency. Fine-tunable via threshold.
Summary
| Method | Distribution | Best for |
|---|---|---|
| LangChain | Uniform | Consistent downstream processing |
| NLTK / spaCy | Smaller + outliers | Linguistic coherence |
| Adjacent Sentence Clustering | Context-sensitive | Thematic coherence with order preservation |
| KMeans | Semantic clusters | Batch/offline processing |