Named Entity Recognition with Python in George Eliot’s The Mill on the Floss

Named Entity Recognition (NER) is a cornerstone of Natural Language Processing (NLP), enabling machines to automatically identify and classify key information in text, such as names of people, organizations, locations, and more. In this blog post, we’ll dive into how you can use Python to perform NER on a classic piece of Victorian literature—George Eliot’s The Mill on the Floss.

Why Analyze Literature with NER?

Literary texts are rich with references to characters, places, historical events, and cultural context. Extracting and analyzing these entities can unlock new perspectives in literary analysis:

  • Character Networks: Understand who interacts with whom.
  • Geographic Mapping: Visualize locations mentioned in the novel.
  • Temporal Analysis: Track when key events or references occur.
  • Theme Exploration: Identify frequently mentioned entities to detect themes.

Getting Started: Setting Up NER in Python

The Python ecosystem offers powerful libraries for NER; two of the most popular are spaCy and NLTK. In this example, we’ll use spaCy due to its speed, accuracy, and user-friendly API.

1. Installing spaCy

pip install spacy
python -m spacy download en_core_web_sm

2. Obtaining the Text

You can download the full text of The Mill on the Floss from resources like Project Gutenberg. Save the text as mill_on_the_floss.txt.

3. Extracting Named Entities

import spacy

# Load English tokenizer, tagger, parser, NER and word vectors  
nlp = spacy.load("en_core_web_sm")

# Read the text of the novel
def get_text(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        return file.read()

# Process the text
text = get_text('mill_on_the_floss.txt')
doc = nlp(text)

# Extract and print entities
for ent in doc.ents:
    print(ent.text, ent.label_)

This code prints every recognized entity and its type—PERSON (for characters), GPE (for locations), ORG (organizations), and more.

Deeper Analysis: Visualizing and Refining

Once you extract entities, you can:

  • Count entity frequency to find the most mentioned characters or places.
  • Visualize relationships using network graphs (try networkx or spaCy's displaCy visualization tools).
  • Disambiguate entities. For example, “Philip” might refer to different characters or contexts—manual checking may be needed for accuracy in a literary text.

Sample Output: Top Characters

from collections import Counter
persons = [ent.text for ent in doc.ents if ent.label_ == 'PERSON']
print(Counter(persons).most_common(10))

This snippet shows which characters are most frequently mentioned, helping you focus your literary analysis.

Challenges in Literary NER

Classic novels like The Mill on the Floss present unique challenges:

  • Archaic Language: Victorian prose can confuse modern NER models.
  • False Positives: Common nouns occasionally misrecognized as names.
  • Nickname Variations: Characters called by several names (e.g., “Maggie,” “Miss Tulliver”); combining these requires custom mapping.
  • Context Dependence: Same word used as both a person and a common noun.

Overcoming these might require annotation, custom training, or manual review, but even basic NER provides a strong head start for textual analysis.

Conclusion

Named Entity Recognition is a powerful tool for exploring the character landscapes and settings within The Mill on the Floss. Python and spaCy make it accessible even for those new to text analysis. Whether you’re a literature enthusiast, a student, or a data scientist, NER offers a compelling new lens through which to discover George Eliot’s intricate world.

Ready to explore deeper? Try applying the same techniques to other novels, or experiment with different NLP libraries for even more refined results!

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