Information extraction patterns in NLP - Model Metrics & Evaluation

Metrics & Evaluation - Information extraction patterns

Which metric matters for Information Extraction Patterns and WHY

Information extraction (IE) aims to find specific pieces of information from text, like names or dates. The key metrics are Precision and Recall. Precision tells us how many extracted items are actually correct. Recall tells us how many of the total correct items we found. We want both high, but sometimes one matters more depending on the task.

Confusion Matrix for Information Extraction

      | Predicted Yes | Predicted No  |
      |---------------|--------------|
      | True Positive | False Negative|
      | False Positive| True Negative |

    TP = Correctly extracted info
    FP = Extracted info that is wrong
    FN = Missed info that should be extracted
    TN = Correctly ignored non-info
    

Precision vs Recall Tradeoff with Examples

If you want to avoid wrong info in your output, focus on high precision. For example, a legal document extractor must not add false facts.

If you want to find all possible info, even if some are wrong, focus on high recall. For example, a news aggregator wants to catch all names mentioned, even if some are mistakes.

Balancing both is key. The F1 score helps measure this balance.

Good vs Bad Metric Values for IE Patterns

Good: Precision and Recall above 0.8 means most extracted info is correct and most info is found.

Bad: Precision below 0.5 means many wrong extractions. Recall below 0.5 means many missed extractions.

Example: Precision=0.9, Recall=0.85 is good. Precision=0.4, Recall=0.3 is bad.

Common Pitfalls in IE Metrics

• Accuracy paradox: High accuracy can be misleading if most text has no info to extract.
• Data leakage: Testing on data too similar to training inflates metrics.
• Overfitting: Model extracts perfectly on training but fails on new text.
• Ignoring class imbalance: Info to extract is rare, so metrics must consider this.

Self Check

Your IE model has 98% accuracy but only 12% recall on extracting names. Is it good?

Answer: No. The model misses most names (low recall), so it is not useful despite high accuracy. It finds very few correct names.

Key Result

Precision and Recall are key metrics for information extraction patterns, measuring correctness and completeness of extracted data.