Word similarity and analogies in NLP - Model Pipeline Trace

Model Pipeline - Word similarity and analogies

This pipeline learns word meanings by looking at many sentences. It then finds how similar words are or solves analogies like 'king is to queen as man is to ?'.

Data Flow - 4 Stages

1Raw Text Data

10000 sentences x variable length→Collect sentences from books and articles→10000 sentences x variable length

"The cat sat on the mat."

↓

2Tokenization

10000 sentences x variable length→Split sentences into words→10000 sentences x variable length (words)

["The", "cat", "sat", "on", "the", "mat"]

↓

3Build Vocabulary

Words from all sentences→Create list of unique words→5000 unique words

["the", "cat", "sat", "on", "mat", "dog", "king", "queen"]

↓

4Word Embedding Training

Sentences with words→Train model to learn word vectors (e.g., Word2Vec)→5000 words x 100 features

"king" vector: [0.25, -0.1, 0.4, ..., 0.05]

Training Trace - Epoch by Epoch

2.5 | *
2.0 |  *
1.5 |   *
1.0 |    *
0.5 |     *
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.3	N/A	Initial training with high loss as model starts learning word contexts
2	1.8	N/A	Loss decreases as word vectors improve
3	1.5	N/A	Model captures better word relationships
4	1.3	N/A	Loss continues to decrease steadily
5	1.2	N/A	Training converges with stable loss

Prediction Trace - 4 Layers

Layer 1: Input word pair vectors

Layer 2: Vector arithmetic for analogy

Layer 3: Find closest word vector

Layer 4: Compute similarity scores

Model Quiz - 3 Questions

What does the vector arithmetic 'queen - king + man' aim to find?

A The word 'king'

B A random word

C A word similar to 'woman'

D The word 'queen'

Key Insight

Word embeddings capture meanings by placing similar words close in space. Vector math on these embeddings can solve analogies, showing how machines learn language relationships.