Splitting Text Right Way - NLTK, SpaCy or Markdown

For natural language processing (NLP) working with large pieces of text can be challenging. Many language models have token limits. If the input text is too large, it needs to be split into smaller pieces before processing - this is called text splitting or chunking. Effective text splitting is important because it ensures that each chunk fits within the model’s limits while still preserving the meaning and context as much as possible. ?

Text splitting also matters in RAG based pipelines. The quality of retrieved information impacts the final generated response. When documents are split into chunks, the retrieval system can better understand and match the content to the user’s query. If chunks are too large, relevant information might get buried inside unnecessary content. If chunks are too small, the meaning could get lost because there isn’t enough context. Chunk overlap helps preserve flow between splits, while length functions ensure each chunk is properly sized for efficient retrieval and model processing. ?

Few key terms which you need to be aware in context of chunking,

For this article we will focus on Langchain text splitters, but you can find similar splitters with other frameworks as well. There are different types of text splitters available under Langchain framework.?

Let's focus on NLTK, SpaCy and Markdown based splitting techniques of text which can help in different text splitting scenarios. ?

NLTK (Natural Language Toolkit) is one of the oldest tools for working with text. Its strength is how well it understands sentence boundaries and paragraph breaks, even when the text gets tricky - like handling abbreviations, special punctuation or non-English content. NLTKTextSplitter takes advantage of this and makes it easy to split large text into clear, natural chunks. This works well when you need to keep sentences together for better meaning, like splitting interviews, articles, or transcripts. It works across different languages and writing styles. NLTK is a good choice as it relies on manmade linguistic rules tuned for different languages and writing styles.?To elaborate let’s try to understand with an example. ?

[1] Take an input text to understand text splitting and overlapping. For this example,?taking a text about Hollywood Moneyball movie review text.

[2] Import NLTK (download nltk punkt_tab data package) and Langchain NLTKTextSplitter package. And initialize NLTKTextSplitter with 200 chunk_size parameter and 100 chunk_overlap parameters. Store chnks by calling the splitter and passing the moneyball_text as input. ?

[3] Analyze the output chunks, if you notice nice overlap of sentence is maintained to keep the context flow intact. ?

SpaCy is a modern NLP library designed for large text processing. Its strength is its understanding of language, it has built-in tokenization, part of speech tagging. It understands the grammar and structure behind the words. It works well for complex documents, technical papers, or content with more grammar and structure. If you want a splitter that’s respects language rules and handles advanced cases like multi-language text, SpaCy is a good option. ?

[1] Take an input text to understand text splitting and overlapping. For this example,?taking a complicated text about AI as input.

[2] Import spacy (load en_core_web_sm english model) and Langchain SpacyTextSplitter package. And initialize SpacyTextSplitter with 500 chunk_size parameter and 100 chunk_overlap parameters. Store chnks by calling the splitter and passing the ai_text as input.

[3] Analyze the output chunks, if you notice in output sentence structure, grammar, technical terms flow is well maintained in chunks. ?

Markdown TextSplitter is useful when you’re working with text that’s written in Markdown format - like documentation, knowledge base articles, or technical guides. Its strength is that it understands the structure of Markdown, so instead of just splitting text randomly, it can break the document into logical sections based on headings like #, ##, and ###. MarkdownTextSplitter takes advantage of this and makes it easy to split large Markdown files into organized chunks that match the natural flow of the content. This is helpful for technical documents where each section stands on its own, like API references or user manuals. If you want to keep your text organized the way the author intended.?

[1] Take an input text to understand text splitting and overlapping. For this example,?taking a complicated markdown text about AI as input.?

[2] Import Langchain SpacyTextSplitter package. And initialize MarkdownTextSplitter with 500 chunk_size parameters and 100 chunk_overlap parameters. Store chnks by calling the splitter and passing the ai_markdown_text as input.?

[3] Analyze the output chunks, if you notice in output appropriate separation of different sections is visible. It is not random text split. ?

Here is the summary of these text splitters,

[Summary]?

Splitting text might seem like a simple step, but it plays a big role in how language models and retrieval systems work. If you split text the right way, you make it easier for the system to find the right information and keep the meaning clear. What’s great about Langchain is that it gives you different splitters for different types of text. If you have regular text like articles or transcripts, NLTK works well because it understands sentences and breaks naturally. If your text is more complex, like a technical paper or something with tricky grammar, SpaCy is a better choice because it actually looks at the structure of the language. And if you’re working with documentation or manuals written in Markdown, the MarkdownTextSplitter is perfect because it knows how to keep sections and headings together. Good splitting means better retrieval, and better retrieval means better answers.? A little extra thought here can make your whole pipeline work smoother and smarter.?