NER with NLTK in NLP - ML Experiment: Train & Evaluate

Experiment - NER with NLTK

Problem:You want to identify named entities like people, places, and organizations in text using NLTK's built-in Named Entity Recognition (NER) tool.

Current Metrics:Accuracy is not directly measured because NLTK's NER uses a pre-trained model, but it often misses some entities or labels them incorrectly.

Issue:The NER model sometimes misses entities or mislabels them, especially in complex sentences or with uncommon names.

Your Task

Improve the recognition of named entities in sample sentences by preprocessing the text and tuning NLTK's NER pipeline.

You must use NLTK's built-in NER and cannot switch to other libraries.

You can only modify preprocessing steps and how you feed data to the NER model.

Hint 1

Hint 2

Hint 3

Solution

NLP

import nltk
from nltk import word_tokenize, pos_tag, ne_chunk, sent_tokenize

# Download required NLTK data
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('maxent_ne_chunker')
nltk.download('words')

# Sample text
text = "Barack Obama was born in Hawaii. He was elected president in 2008. Microsoft is a big company located in Redmond."

# Step 1: Sentence tokenize
sentences = sent_tokenize(text)

# Step 2: For each sentence, tokenize words, POS tag, then apply NER
for sentence in sentences:
    tokens = word_tokenize(sentence)
    pos_tags = pos_tag(tokens)
    named_entities = ne_chunk(pos_tags)
    print(named_entities)

# The output shows named entities as tree structures with labels like PERSON, GPE, ORGANIZATION

Added sentence tokenization to split text into smaller parts for better context.

Applied word tokenization and POS tagging before NER to improve entity recognition.

Cleaned the text by removing unnecessary characters (implicitly by tokenization).

Results Interpretation

Before: Applying NER on raw text without sentence splitting or POS tagging often misses or mislabels entities.

After: Using sentence tokenization, word tokenization, and POS tagging before NER improves entity detection accuracy and labeling.

Proper preprocessing like sentence splitting and POS tagging helps NLTK's NER model understand context better, leading to more accurate named entity recognition.

Bonus Experiment

Try adding custom named entity patterns using NLTK's RegexpParser to recognize entities not detected by the default NER.

💡 Hint

Use chunk grammar rules to define patterns for entities like dates, product names, or titles.