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POS Tagging & Named Entity Recognition

Two applied NLP notebooks analyzing literary text: a general POS-tagging and NER assignment, plus a focused frequency analysis of a scene from The Lord of the Rings: The Return of the King.

Predictive Analytics · Coursework NLP Individual

Overview

Both notebooks work through the same core pipeline — text cleaning, Part-of-Speech (POS) tagging, and frequency-based interpretation — applied to hand-picked literary passages.

POS Tagging & NER takes a paragraph from a chosen piece of literature, cleans it, tags each token's part of speech, and analyzes POS frequency to determine whether the passage is noun-heavy, verb-heavy, or otherwise, alongside named entity extraction.

LOTR POS Analysis narrows the same method onto a single, well-known scene — Aragorn's "My friends, you bow to no one" speech from The Return of the King — cleaning the text, tagging it, and interpreting the POS distribution against the tone of the passage.

Notebook: POS Tagging & NER

pos_tagging_and_ner.html Open full size ↗

Notebook: LOTR POS Analysis

lotr_pos_analysis_simple.html Open full size ↗

Tech stack

PythonNotebook environment
NLTK / spaCyPOS tagging & NER
Text PreprocessingCleaning & tokenization
Frequency AnalysisInterpretation
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