Shallow syntax

Shallow syntactic tasks are NLP algorithms that provide an analysis of a text on the level of the syntactic structure of the text by performing analysis of the grammatical structure of a sentence at a basic level, often focusing on the surface form and immediate dependencies without delving into deeper semantic or contextual understanding.

Key components:

  • Part-of-Speech (POS) Tagging: Identifying and labeling the grammatical categories (nouns, verbs, adjectives, etc.) of words in a sentence.
  • Chunking: Grouping words into syntactically related phrases or "chunks," such as noun phrases or verb phrases.
  • Named Entity Recognition (NER): Identifying and classifying named entities (e.g., names of people, organizations, locations) in a text.
  • Dependency Parsing: Analyzing the grammatical relationships and dependencies between words in a sentence, typically represented in the form of a dependency tree.


  • Shallow syntax analysis provides a foundational understanding of the grammatical structure of a sentence, which is essential for many NLP tasks such as Information Extraction (IE), Summarization, and Machine Translation.
  • While shallow syntax algorithms do not capture deep semantic nuances, they are computationally efficient and can serve as a precursor to more in-depth linguistic analysis.
  • Shallow syntax analysis is often used as a Pre-Processing step in NLP pipelines to extract basic linguistic features before applying more complex semantic or contextual models.