Calculates similarity between documents using traditional NLP methods or modern embedding-based approaches. Comprehensive metrics are automatically computed unless disabled.
Usage
calculate_document_similarity(
texts,
document_feature_type = "words",
semantic_ngram_range = 2,
similarity_method = "cosine",
use_embeddings = FALSE,
embedding_model = "all-MiniLM-L6-v2",
calculate_metrics = TRUE,
verbose = TRUE
)Arguments
- texts
A character vector of texts to compare.
- document_feature_type
Feature extraction type: "words", "ngrams", "embeddings", or "topics".
- semantic_ngram_range
Integer, n-gram range for ngram features (default: 2).
- similarity_method
Similarity calculation method: "cosine", "jaccard", "euclidean", "manhattan".
- use_embeddings
Logical, use embedding-based similarity (default: FALSE).
- embedding_model
Sentence transformer model name (default: "all-MiniLM-L6-v2").
- calculate_metrics
Logical, compute comprehensive similarity metrics (default: TRUE).
- verbose
Logical, if TRUE, prints progress messages.
Value
A list containing:
- similarity_matrix
N x N similarity matrix
- feature_matrix
Document feature matrix used for calculation
- method_info
Information about the method used
- metrics
Comprehensive similarity metrics (if calculate_metrics = TRUE)
- execution_time
Time taken for analysis
Examples
if (interactive()) {
texts <- c(
"Assistive technology supports learning for students with disabilities.",
"Technology aids help disabled students with their education.",
"Machine learning algorithms improve predictive accuracy."
)
result <- calculate_document_similarity(
texts = texts,
document_feature_type = "words",
similarity_method = "cosine"
)
print(result$similarity_matrix)
print(result$metrics)
}