Shallow Parsing vs Deep Parsing in NLP (with Examples)

Shallow parsing (chunking) VS Deep parsing

In Natural Language Processing (NLP), parsing refers to the process of analyzing the grammatical structure of a sentence. Based on the depth of syntactic analysis required, parsing techniques are broadly classified into shallow parsing and deep parsing.

Shallow parsing (chunking) focuses on identifying flat, non-recursive phrase units such as noun phrases (NP), verb phrases (VP), and prepositional phrases (PP). It avoids building complete parse trees, making it computationally efficient, robust to noise, and suitable for large-scale NLP applications.

In contrast, deep parsing (full parsing) aims to construct a complete syntactic structure of a sentence by capturing hierarchical relationships and long-distance dependencies. Although linguistically richer, deep parsing is more computationally expensive and sensitive to errors.

The visuals and comparison table below clearly illustrate the conceptual and practical differences between shallow parsing and deep parsing.

Comparison of Shallow Parsing (Chunking) and Deep Parsing (Full Parsing) in NLP

Aspect	🟢 Shallow Parsing (Chunking)	🔵 Deep Parsing (Full Parsing)
🎯 Main Goal	Identify simple (flat) phrase chunks	Build complete syntactic structure
📦 Output	Flat, non-recursive phrases (NP, VP, PP)	Hierarchical parse trees
🔁 Recursion	Not allowed	Allowed
📐 Phrase Overlap	No overlapping chunks	Overlapping via tree structure
🪜 Levels of Analysis	Partial / Surface levels	Complete syntactic analysis
⚙️ Grammar Used	Regular expressions, FSA, FST	CFGs, dependency grammars
⚡ Speed	Fast & efficient	Slower
⏱️ Computational Cost	Low	High
🛡️ Error Tolerance	Robust to POS errors	Sensitive to errors
📈 Scalability	Suitable for large datasets	Not scalable for large corpora
🧠 Learning Models	Rule-based, CRFs, HMMs	Probabilistic & neural parsers
📚 Training Data	Minimal or optional	Large annotated treebanks
🔗 Long-distance Dependencies	Not handled	Handled
🧩 Semantic Understanding	Limited	Strong
🧪 Typical Use Cases	Information extraction, preprocessing	Machine translation, grammar checking
📝 Example Task	Base noun phrase detection	Subject–object dependency analysis
📝 Examples	[NP The quick brown fox] [VP jumps] [PP over] [NP the lazy dog]	Subject–object dependency analysis

📌 Shallow Parsing (Chunking) Example

Consider the sentence: "The quick brown fox jumps over the lazy dog."

After POS tagging (DT = determiner, JJ = adjective, NN = noun, VBZ = verb), chunking identifies non-overlapping, flat phrase chunks.

Original POS Sequence	Chunked Output
The/DT quick/JJ brown/JJ fox/NN	[The quick brown fox] → NP
jumps/VBZ	[jumps] → VP
over/IN	[over] → PP
the/DT lazy/JJ dog/NN	[the lazy dog] → NP

📌 Deep Parsing Example

Using the same sentence: "The quick brown fox jumps over the lazy dog."

Deep parsing performs a complete syntactic analysis by generating a full constituency parse tree or a dependency parse. Unlike shallow parsing, it reveals hierarchical structure and nesting.

🔹 Constituency Parse Tree

This parse tree reveals nested phrase structure:

• The noun phrase NP ("The quick brown fox") functions as the subject.
• The verb phrase VP contains a prepositional phrase PP ("over the lazy dog") acting as an object modifier.