There are several common evaluation metrics used for sentence segmentation.

  1. Precision, Recall, and F1-score: Precision measures the proportion of correctly segmented sentences out of the total number of sentences identified by the system. Recall measures the proportion of correctly segmented sentences out of the total number of sentences in the reference data. F1-score is the harmonic mean of precision and recall, providing a balanced measure of segmentation performance.

  2. Sentence Level Accuracy: It calculates the proportion of correctly segmented sentences out of the total number of sentences in both the system output and the reference data.

  3. Boundary Similarity: This metric measures the similarity between the boundaries of the system output and the reference sentences. It can be calculated using various similarity measures like Levenshtein distance or F-measure.

  4. Word Error Rate (WER): WER measures the proportion of word-level errors in the segmented sentences. It considers both false positives (extra sentences) and false negatives (missing sentences).

  5. Boundary Fragmentation and Over-segmentation: These metrics focus on measuring the extent of boundary errors in the system output, including fragmentation (where sentences are divided into smaller parts) and over-segmentation (where multiple sentences are incorrectly merged).

  6. Boundary Detection Accuracy: It calculates the proportion of correctly identified sentence boundaries in the system output.

  7. Boundary Identification Recall: This metric measures the proportion of correctly identified sentence boundaries out of the total number of boundaries in the reference data.

  It is important to note that different evaluation metrics have their own advantages and limitations. Researchers and practitioners should select appropriate metrics based on their specific requirements and the characteristics of the sentence segmentation task at hand.