Document similarity ranking in NLP - Model Pipeline Trace

Model Pipeline - Document similarity ranking

This pipeline finds how similar documents are to a query document. It ranks documents by similarity scores, helping find the closest matches.

Data Flow - 6 Stages

1Data in

1000 documents x variable length text→Raw text documents collected for similarity search→1000 documents x variable length text

"Doc1: The cat sat on the mat.", "Doc2: Dogs are friendly animals."

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2Preprocessing

1000 documents x variable length text→Lowercase, remove punctuation, tokenize words→1000 documents x list of tokens

"doc1: ['the', 'cat', 'sat', 'on', 'the', 'mat']"

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3Feature Engineering

1000 documents x list of tokens→Convert tokens to TF-IDF vectors→1000 documents x 5000 features

Doc1 vector: [0.1, 0.0, 0.3, ..., 0.0]

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4Model Training

Training pairs of document vectors with similarity labels→Train a cosine similarity model or neural network to score similarity→Trained similarity scoring model

Model learns to output higher scores for similar document pairs

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5Metrics Improve

Validation document pairs→Evaluate ranking metrics like Mean Average Precision (MAP)→MAP score improves from 0.5 to 0.85

Epoch 1 MAP=0.5, Epoch 5 MAP=0.85

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6Prediction

Query document vector and 999 document vectors→Compute similarity scores and rank documents→Ranked list of documents by similarity

Query: Doc1, Top matches: Doc5 (0.92), Doc20 (0.89), Doc3 (0.85)

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |**  
0.3 |*   
0.2 |*   
0.1 |    
    +-----
     1 5  Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.55	Model starts learning, loss high, accuracy low
2	0.48	0.68	Loss decreases, accuracy improves
3	0.35	0.78	Model learns better similarity patterns
4	0.28	0.83	Loss continues to drop, accuracy rises
5	0.22	0.87	Model converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Query document vectorization

Layer 2: Similarity scoring

Layer 3: Ranking

Model Quiz - 3 Questions

What happens to the data shape after converting text to TF-IDF vectors?

A From numeric vectors to raw text

B From variable length text to fixed length numeric vectors

C From fixed length vectors to variable length text

D No change in data shape

Key Insight

Document similarity ranking uses vector representations and similarity scores to find and order documents by how close their meaning is to a query. Training improves the model's ability to assign higher scores to truly similar documents, making search results more relevant.