What is Chunking?

Chunking (shallow parsing) is the process of dividing a sentence into syntactic groups—such as noun phrases (NP), verb phrases (VP), and prepositional phrases (PP)—without building a full parse tree. Chunks are non-overlapping, non-recursive groups of words that form meaningful units, useful for downstream NLP tasks.

Visualizing Chunking vs. Full Parsing

• Chunker: Divides sentences into meaningful groups (chunks), e.g., [NP the current account deficit], [VP will narrow].
• Full Parser: Builds a complete syntactic tree, showing all grammatical relationships.

Why Build a Chunker?

• Simplifies sentence structure for information extraction, question answering, and more
• Faster and more robust than full parsing
• Useful for languages with complex syntax

Machine Learning Approaches

Hidden Markov Model (HMM)

• Probabilistic model predicting the most likely sequence of chunk tags
• Uses transition probabilities and observed word/tag likelihoods
• Effective for tasks with local context dependencies

Conditional Random Field (CRF)

• Discriminative model for structured prediction
• Incorporates a wider range of features and dependencies
• Often more accurate for chunking, especially with rich feature sets

Role of Features and Corpus Size

• Feature Selection: Lexicon, POS tags, or both; richer features improve accuracy
• Training Corpus Size: Larger corpora provide more examples, generally increasing accuracy

Summary

In this experiment, you will build chunkers using HMM and CRF, experiment with features and corpus size, and analyze how these choices affect chunking accuracy. The simulation provides hands-on experience with model configuration and performance evaluation.