Limitations of classical methods in NLP - Model Pipeline Trace

Model Pipeline - Limitations of classical methods

This pipeline shows how classical NLP methods process text data and highlights their limitations in understanding language deeply.

Data Flow - 6 Stages

1Data in

1000 sentences→Raw text input→1000 sentences

"I love sunny days."

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2Preprocessing

1000 sentences→Tokenization and stopword removal→1000 sentences with tokens

["love", "sunny", "days"]

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3Feature Engineering

1000 sentences with tokens→Bag-of-Words vectorization→1000 rows x 5000 columns

Vector with counts of words like {"love":1, "sunny":1, "days":1, ...}

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4Model Trains

1000 rows x 5000 columns→Train classical classifier (e.g., Naive Bayes)→Trained model

Model learns word probabilities for classes

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5Metrics Improve

Validation set vectors→Evaluate accuracy and loss→Accuracy: 0.75, Loss: 0.5

Model correctly classifies 75% of validation sentences

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6Prediction

New sentence vector→Model predicts class→Predicted label

"Positive sentiment"

Training Trace - Epoch by Epoch

Loss
0.7 | *       
0.6 |  **     
0.5 |   ***   
    +--------
     1 2 3 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning word patterns
2	0.55	0.68	Accuracy improves as model fits data better
3	0.50	0.75	Model converges but limited by simple features

Prediction Trace - 4 Layers

Layer 1: Tokenization

Layer 2: Stopword Removal

Layer 3: Bag-of-Words Vectorization

Layer 4: Model Prediction

Model Quiz - 3 Questions

What is a main limitation of classical NLP methods shown in this pipeline?

A They require very large datasets

B They ignore word order and context

C They always overfit the training data

D They use deep neural networks

Key Insight

Classical NLP methods use simple word counts and ignore word order and context, limiting their ability to understand language deeply. This causes accuracy to plateau early and limits performance on complex tasks.