Dependency parsing in NLP - Model Pipeline Trace

Model Pipeline - Dependency parsing

Dependency parsing finds how words in a sentence connect to each other. It shows which words depend on others, like a family tree for sentences.

Data Flow - 4 Stages

1Input sentence

1 sentence, 6 words→Raw text input→1 sentence, 6 tokens

"She enjoys reading books daily."

↓

2Tokenization

1 sentence, 6 words→Split sentence into tokens (words)→1 sentence, 6 tokens

["She", "enjoys", "reading", "books", "daily", "."]

↓

3Feature extraction

1 sentence, 6 tokens→Convert tokens to word embeddings (numerical vectors)→1 sentence, 6 tokens x 100 features

[[0.12, -0.05, ...], [0.33, 0.01, ...], ...]

↓

4Dependency parsing model

1 sentence, 6 tokens x 100 features→Neural network predicts head word and relation for each token→1 sentence, 6 tokens with head indices and relation labels

[{"word": "She", "head": 2, "relation": "nsubj"}, {"word": "enjoys", "head": 0, "relation": "root"}, ...]

Training Trace - Epoch by Epoch

Loss
1.2 |****
1.0 |***
0.8 |**
0.6 |**
0.4 |*
0.2 |
0.0 +----------------
      1  2  3  4  5  Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.20	0.55	Model starts learning basic word relations.
2	0.85	0.70	Accuracy improves as model learns syntax patterns.
3	0.60	0.80	Model captures more complex dependencies.
4	0.45	0.85	Loss decreases steadily; model generalizes well.
5	0.35	0.90	Training converges with high accuracy.

Prediction Trace - 3 Layers

Layer 1: Input tokens

Layer 2: Word embeddings

Layer 3: Neural network parsing

Model Quiz - 3 Questions

What does the 'head' index represent in dependency parsing?

A The position of the word in the sentence

B The word that the current word depends on

C The length of the word

D The frequency of the word in the sentence

Key Insight

Dependency parsing models learn to map words to their syntactic heads and relations, helping machines understand sentence structure. Training shows steady improvement as the model captures language rules.