When working with text data in natural language processing (NLP), how we split or tokenize the data plays a crucial role in the performance of the downstream applications. In this post, we'll explore the major text splitting mechanisms, their benefits, downsides, illustrative examples, and typical use cases.

1. Whitespace Tokenization

  • Pros: Simple and fast; effective for languages with clear word boundaries.
  • Cons: Unsuitable for languages without word delimiters.
  • Example: "I love AI." -> ["I", "love", "AI."]
  • Use Case: Basic text processing, topic modeling, bag-of-words models.

2. Sentence Splitting

  • Pros: Maintains context; useful for tasks requiring sentence-level parsing.
  • Cons: Can be fooled by abbreviations or unconventional punctuation.
  • Example: "Dr. Smith went to Paris. He enjoyed it." -> ["Dr. Smith went to Paris.", "He enjoyed it."]
  • Use Case: Summarization, per-sentence sentiment analysis.

3. Morphological Tokenization

  • Pros: Captures linguistic nuances.
  • Cons: Requires profound linguistic understanding.
  • Example: "running" -> ["run", "-ing"]
  • Use Case: Modeling for morphologically-rich languages, linguistic research.

4. Subword Tokenization

  • Pros: Addresses out-of-vocabulary words.
  • Cons: Requires corpus training; tokens may lack semantic clarity.
  • Example: "ChatGPT" using BPE -> ["Chat", "G", "PT"]
  • Use Case: Machine translation, modern NLP models like BERT and GPT.

5. Character Tokenization

  • Pros: Eliminates out-of-vocabulary issues; captures character-level patterns.
  • Cons: Generates long sequences for lengthy texts.
  • Example: "AI" -> ["A", "I"]
  • Use Case: Text generation, spelling correction.

6. N-gram Tokenization

  • Pros: Captures local context.
  • Cons: Can produce numerous features; might miss global context.
  • Example: "I love AI" with 2-grams -> ["I love", "love AI"]
  • Use Case: Text classification, plagiarism detection.

7. Regular Expression Tokenization

  • Pros: Highly customizable.
  • Cons: Can become intricate; needs regex knowledge.
  • Example: Extracting hashtags: "#AI is great" -> ["#AI"]
  • Use Case: Information extraction, domain-specific tasks.

8. Chunking

  • Pros: Produces meaningful chunks.
  • Cons: Requires part-of-speech tagging.
  • Example: "The big cat" -> ["The big cat"]
  • Use Case: Relation extraction, shallow parsing.

9. Heuristic-based Tokenization

  • Pros: Tailored for specific tasks.
  • Cons: Might not generalize; can become complex.
  • Example: Splitting on "and" -> "Tom and Jerry" -> ["Tom", "Jerry"]
  • Use Case: Domain-specific applications, custom data extraction.

10. Recursive Splitting

  • Pros: Suitable for processing long texts.
  • Cons: Can disrupt context without overlaps.
  • Example: Splitting "HelloWorld" into chunks of 5 with overlap of 2 -> ["Hello", "lloWo", "oWorld"]
  • Use Case: Processing extended documents in models with input size limits.

Conclusion: Selecting the right text splitting mechanism can make a significant difference in NLP tasks. Understanding the nature of your data, the specifics of the application, and the nuances of different tokenization methods can help optimize performance and outcomes.