Dependency parsing in NLP - Model Metrics & Evaluation

Metrics & Evaluation - Dependency parsing

Which metric matters for Dependency Parsing and WHY

Dependency parsing finds the relationships between words in a sentence. The key metrics are Unlabeled Attachment Score (UAS) and Labeled Attachment Score (LAS).

UAS measures how many words are correctly linked to their parent word, ignoring the type of relation. LAS measures how many words are correctly linked with the right relation type.

These metrics matter because they show how well the model understands sentence structure and meaning.

Confusion Matrix or Equivalent Visualization

Dependency parsing does not use a classic confusion matrix like classification. Instead, we count correct and incorrect arcs (links) between words.

Total words: 1000
Correct head links (UAS): 900
Incorrect head links: 100
Correct labeled links (LAS): 850
Incorrect labeled links: 150

UAS = 900 / 1000 = 0.90 (90%)
LAS = 850 / 1000 = 0.85 (85%)
    

This shows 90% of words have the correct parent word, and 85% have the correct parent and relation type.

Precision vs Recall Tradeoff in Dependency Parsing

Dependency parsing focuses on accuracy of links, so precision and recall are less common metrics here. But if we think of arcs as predictions:

• Precision: How many predicted links are correct?
• Recall: How many true links did the model find?

High precision means few wrong links, high recall means few missed links. A parser with high precision but low recall misses many relations, while one with high recall but low precision adds many wrong links.

In practice, UAS and LAS balance these aspects by measuring correct links over total words.

What Good vs Bad Metric Values Look Like

Good dependency parsers have UAS and LAS above 85% on standard datasets.

• Good: UAS = 90%, LAS = 85% or higher. This means most words are linked correctly with the right relation.
• Bad: UAS below 70%, LAS below 60%. This means many words are linked incorrectly or with wrong relations, making sentence understanding poor.

Higher LAS is harder to achieve because it requires correct relation types, not just correct links.

Common Metrics Pitfalls in Dependency Parsing

• Ignoring relation labels: Reporting only UAS hides errors in relation types, which are important for meaning.
• Data leakage: Training and testing on overlapping sentences inflates scores falsely.
• Overfitting: Very high scores on training but low on test data show the model memorizes rather than generalizes.
• Ignoring sentence length: Longer sentences are harder to parse; average scores may hide poor performance on complex sentences.

Self Check: Your model has 98% UAS but 50% LAS. Is it good?

No, this means the model finds the correct parent word for most words (98%), but only half the time it assigns the correct relation type (50%).

This is a problem because understanding sentence meaning depends on correct relations, not just links.

You should improve the model to raise LAS closer to UAS for better real-world use.

Key Result

Unlabeled Attachment Score (UAS) and Labeled Attachment Score (LAS) are key metrics showing how well a dependency parser finds correct word links and relation types.