In Natural Language Processing (NLP), chunking refers to the process of segmenting a sentence into syntactically correlated parts, or “chunks,” such as noun phrases (NPs), verb phrases (VPs), and prepositional phrases (PPs). It sits between part-of-speech (POS) tagging and full syntactic parsing, offering a shallow, yet informative, structural representation of text.
Key Characteristics:
- Shallow Parsing: Unlike full parsing, chunking doesn’t analyze hierarchical grammatical relationships—only flat groupings of words.
- Phrase Detection: Focuses on identifying groups of words that function together, such as “the red car” (a noun phrase).
- Uses POS Tags: Relies heavily on part-of-speech tagging to determine phrase boundaries.
- BIO Tagging Scheme: Commonly uses Beginning-Inside-Outside (BIO) tags to mark phrase segments.
Example:
For the sentence: “The quick brown fox jumps over the lazy dog.”
A chunked version might look like: [NP The quick brown fox] [VP jumps] [PP over] [NP the lazy dog]
Applications:
- Information Extraction: Identifies meaningful chunks (e.g., names, dates, locations) for downstream tasks.
- Question Answering: Helps isolate relevant entities and phrases in candidate answers.
- Named Entity Recognition (NER): Often used as a preprocessing step to improve NER accuracy.
- Grammar Correction and Text Simplification: Assists in understanding structure for better rewriting or correction
Why It Matters:
Chunking simplifies sentence structure in a computationally efficient way. It provides structural insights without the complexity of full parsing—ideal for tasks that require basic syntactic understanding without full grammatical analysis.
