Chunking Strategies for Entity and Relationship Extraction

If your pipeline can find names, dates, organizations, or products but still struggles to connect them, the issue often begins before the model even runs. Relationship extraction depends heavily on chunk boundaries, since the evidence for a fact is often spread across nearby sentences or even a whole section.

This is why document-level relation extraction is now a research area in its own right. DocRED focuses on relations that require multi-sentence reading; DyGIE++ handles both local and cross-sentence context; and recent surveys continue to highlight that coreference and long-span reasoning are major challenges in document-level information extraction.

At its core, named entity recognition (NER) finds mentions of real-world objects and labels them, while relation extraction connects those mentions into structured links. In information extraction, this process is about turning unstructured text into structured data. Recent NER and RE surveys show that these tasks are central to search, knowledge base building, question answering, and other NLP workflows. Chunking is important because it determines whether the evidence for these records stays together or is split before the model processes them.

Why Chunking Still Matters In The Long-Context Era

Even with longer context windows, careful chunking is still needed. Longformer showed that models can handle longer documents better, but having more input alone does not guarantee the model will use the evidence effectively.

Lost in the Middle showed that models can perform worse when important information is hidden deep in long contexts. Google’s LangExtract handles long documents by chunking at clear boundaries, processing in parallel, and making several passes over smaller sections rather than a single big pass. In practice, chunking still improves recall, not just a way to manage token limits.

A good chunk does more than just fit the model’s input size. It should keep the entity span together, keep the relation cue close to the entity pair, maintain the heading or layout context that gives meaning, and ensure every extraction can be traced back to the source. Structure-aware splitters and layout-aware parsers follow this approach. For example, LangChain keeps paragraphs and sentences together, Azure chunks by document structure and meaning, Google’s layout parser keeps heading ancestry, and tools like Unstructured and Docling build chunking on parsed document elements and metadata, not just plain text.

This challenge is not limited to LLMs. In the classic information extraction pipeline described by Jurafsky and Martin, distant supervision for relation extraction starts with named-entity tagging across large datasets, then collects sentences containing matching entity pairs. The relation classifier uses features such as surrounding words, entity types, and dependency patterns. spaCy also treats sentence segmentation as a key preprocessing step. If boundaries are broken early, the information needed for relation extraction becomes less clear before modeling even starts.

Chunking Strategies That Actually Help

Fixed-Size Chunking

Fixed-size chunking remains a useful baseline because it is simple, fast, and easy to scale. Recent taxonomy work still treats it as one of the core structure-based strategies. But for entity extraction and relationship extraction, it is rarely the best default. A strict cutoff can split a relation cue from its argument, separate a paragraph from its heading, or break a table row in half. This makes fixed-size chunking a fine starting benchmark, but not a good long-term strategy for high-recall extraction.

Sentence-Based Chunking

Sentence-based chunking is usually a safer starting point for short articles, emails, notes, and other writing where most facts are found in one or two sentences. LangChain’s generic splitter tries to keep paragraphs, then sentences, then words together. LlamaIndex offers sentence-window parsing to keep the local context close to each sentence. This approach gives cleaner entity boundaries and keeps nearby evidence available for relationship extraction.

Sliding Windows And Overlap

When relations often cross into nearby sentences, the solution is not to make every chunk very large. Instead, it is better to add a small overlap or use a sentence window. LlamaIndex’s sentence-window parser saves each sentence along with its neighbors in metadata, which helps when one sentence introduces the entities and the next gives the action, cause, or attribute. This matches document-level RE findings, where cross-sentence context often holds the key evidence.

Paragraph- And Section-Aware Chunking

For reports, policies, manuals, research papers, and long articles, chunking by paragraphs or sections is usually better than splitting by length alone. LangChain’s recursive splitter keeps paragraph units together, Azure’s layout-based chunking uses headings and meaning, and Google’s Gemini layout parser keeps track of which paragraphs belong to which heading and can include heading context in the chunk. When a sentence says “the company,” “the treatment,” or “the following event,” the heading above it may be the only thing that makes the entity or relation clear.

Layout-Aware Chunking For PDFs, Tables, And Complex Documents

If your data includes many PDFs, forms, filings, slide decks, or tables, layout-aware chunking is essential. Google points out that standard OCR flattens documents and removes structure, while Azure, Unstructured, and Docling all focus on chunking based on detected elements and metadata. This matters for extraction because a table header, section label, caption, or unit marker may provide the relation type, time period, or measurement context needed to understand the entity pair.

Semantic And Dynamic Chunking

Semantic or LLM-guided chunking is helpful when topic changes do not match sentence counts or page breaks. For example, LumberChunker splits text where the content shifts, making chunks more independent by meaning. This can help with narrative or mixed-topic documents. However, it should be used carefully. A recent 2026 preprint found that chunking performance depends on the task. Simple structure-based methods often work better for standard in-corpus retrieval, while LumberChunker did better for in-document retrieval. If your extraction pipeline uses retrieval to gather evidence, this difference is important.

Late Chunking And Contextualized Chunks

Late chunking is another strong idea for retrieval-backed extraction. Instead of embedding each small chunk independently, the late chunking paper encodes the long text first and applies chunking later, so each chunk representation carries a fuller context. That can improve retrieval, especially when isolated chunks lose meaning without their neighbors. But it is not automatically better everywhere: the same 2026 benchmark suggests contextualized chunking helps some retrieval settings and hurts others. It is best treated as a targeted optimization, not a universal replacement for good segmentation.

A Practical Setup Workflow For Better Extraction

The most reliable setup is usually layered. First, break the document into meaningful elements instead of flattening it right away. Second, pick a base unit that matches the source: use sentences for short text, paragraphs or sections for longer documents, and layout blocks for PDFs and tables.

Third, maintain local context by using small windows or heading metadata rather than just making chunks bigger. Fourth, run extraction within those chunks. Tools and documentation from Docling, Unstructured, Google Document AI, LlamaIndex, and LangExtract all support this structure-first, context-aware workflow.

After extracting locally, consolidate at the document level. The document-level IE survey notes that entity coreference remains a challenge, and that spaCy’s entity linking tools map text mentions to standard knowledge-base identifiers.

A good production setup, based on survey literature and current tools, is to extract mentions locally, then merge aliases, repeated mentions, and linked entities globally before saving final relationships. This separates local evidence gathering from global entity cleanup.

Before retraining the model, evaluate the chunker itself. Check how often gold entities are split across chunk boundaries, how often true relations end up in separate chunks with no shared window, whether headings or table headers were removed, and whether every extracted fact can be traced back to the source text.

LangExtract stresses the importance of exact source grounding for this reason. Recent surveys on document-level IE indicate that reasoning and coreference errors remain the main challenges. If you do not measure boundary loss, you might mistake a data-preparation problem for a modeling problem.

Common Mistakes That Quietly Damage Extraction Quality

The first mistake is thinking that the biggest possible context is always best. Longformer shows why long inputs matter, but Lost in the Middle and Google’s extraction tools teach the same lesson: long context only helps if the model can actually use the evidence inside it. Bigger is not always better.

The second mistake is removing structure too early. If you take out headings, list hierarchy, table headers, and page-level context, later steps have to rebuild the meaning that was already there. This is why layout-aware parsers and structure-aware chunkers keep this context with each chunk.

The third mistake is using the same chunking method for every dataset. Recent benchmarks show there is no single best strategy. The right approach depends on whether you need sentence-level extraction, document-level reasoning, or retrieval-based evidence gathering. In practice, starting with sentence- or paragraph-aware chunking, adding small context windows, and doing document-level consolidation is safer than using tiny fragments or huge chunks for everything.

Final Takeaway

The best chunk is not the smallest bit of text or the largest your model can handle. It is the smallest unit that still keeps the fact you want to extract: the entity span, the relation cue, the nearby explanation, and the structure that makes the relation clear.

For most real-world pipelines, this means starting with sentence- or paragraph-aware splitting, using layout-awareness for complex documents, adding narrow windows across relation boundaries, and finishing with document-level consolidation. When chunking is set up this way, entity extraction is cleaner, relationship extraction is more complete, and the final knowledge is easier to check and trust.

References

1. Daniel Jurafsky and James H. Martin, Speech and Language Processing (online book, 3rd ed.), including Chapter 20 on information extraction.

2. Yao et al., DocRED: A Large-Scale Document-Level Relation Extraction Dataset.

3. Wadden et al., Entity, Relation, and Event Extraction with Contextualized Span Representations (DyGIE++).

4. Zheng, Wang, and Huang, A Comprehensive Survey on Document-Level Information Extraction.

5. Detroja, Bhensdadia, and Bhatt, A Survey on Relation Extraction.

6. Beltagy, Peters, and Cohan, Longformer: The Long-Document Transformer.

7. Liu et al., Lost in the Middle: How Language Models Use Long Contexts.

8. Google Developers Blog, Introducing LangExtract: A Gemini-powered information extraction library.

9. Google LangExtract docs, long-document example, and chunking notes.