Evaluating cohesion and coherence in texts, especially those generated by AI, is crucial for ensuring quality communication and understanding. Here are some commonly used metrics and methods for assessing these aspects:
Cohesion Metrics – Cohesion refers to the linguistic aspects that connect sentences and ideas within a text. Here are some metrics for evaluating cohesion:
1. **N-gram Overlap:**
– Measures the extent to which n-grams (sequences of n words) in one part of the text appear in another. Higher overlap suggests better cohesion.
2. **Cosine Similarity:**
– Computes the cosine of the angle between two vector representations of text segments (like sentence embeddings). A higher cosine similarity indicates a greater semantic connection.
3. **Lexical Cohesion:**
– Assesses the use of related or repeated words throughout the text. Metrics may include count of repeated keywords or use of synonyms.
4. **Reference Resolution:**
– Analyzes how effectively pronouns and other referring expressions link back to previously mentioned entities. A high resolution rate indicates better cohesion.
5. **Connective Use:**
– Evaluates the presence and effectiveness of discourse connectives (e.g., “however,” “therefore”) that link sentences and paragraphs.
### Coherence Metrics
Coherence refers to the overall logical flow and clarity of ideas in a text. Metrics for evaluating coherence include:
1. **Topic Coherence:**
– Measures how closely related the main topics are throughout the text. This can be assessed using topic modeling techniques like Latent Dirichlet Allocation (LDA) or coherence scores (like UMass or UCI).
2. **Graph-Based Models:**
– Constructs a graph where nodes are sentences and edges represent relationships. Metrics like PageRank can help identify how well sentences relate to each other.
3. **Human Annotator Evaluation:**
– Involves subjective evaluation by human raters who assess text coherence on a defined scale. While subjective, this method can provide deep insights.
4. **Sentence Order and Transition Analysis:**
– Evaluates whether the narrative flow logically progresses from one idea to another, potentially using transition metrics (e.g., evaluating transitions between paragraphs).
5. **Semantic Similarity:**
– Analyses the semantic alignment between sentences using techniques like BERT embeddings to understand if sentences relate logically.
### Tools and Frameworks
– **BERTScore:**
Estimates the semantic similarity between generated text and reference texts based on BERT embeddings, useful for both cohesion and coherence.
– **Discourse Connectives Analysis:**
Tools that analyze the use of structured discourse markers can help in evaluating cohesive and coherent text.
– **Text Cohesion and Coherence Analyzer (TC2):**
A specific tool designed to assess the quality of text with regard to cohesion and coherence.
### Conclusion
A combination of these metrics and methods can provide a robust evaluation of cohesion and coherence in AI-generated texts. While automated metrics can offer quick assessments, integrating human judgment is often necessary for a more nuanced understanding.
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