nearduplicateMethoden

Near-duplicate methods refer to techniques for identifying items that are substantially similar but not identical in content. They are widely used in information retrieval, data cleaning, web indexing, and digital repositories to reduce redundancy, improve search quality, and prevent content sprawl. The focus is on detecting near-duplicates, where texts or items share high similarity despite minor edits, paraphrasing, or reformatting.

Common approaches combine feature extraction with similarity assessment. Shingling creates sequences of tokens or characters from documents, which are then condensed into compact representations such as MinHash fingerprints or SimHash to enable scalable comparison through locality-sensitive hashing. Similarity can be measured with Jaccard or cosine metrics on the vectorized representations, or with edit-distance based measures for exact string comparisons. Character n-grams often capture paraphrastic variants more robustly than word-based features.

Systems typically follow a two-stage pipeline: a blocking or candidate-generation stage reduces the number of potential pairs, followed by a more accurate verification stage that computes detailed similarities and applies a threshold to declare near-duplicates. Blocking criteria may rely on metadata, initial tokens, or clustering results. Thresholds mediate precision and recall and may be tuned for specific domains or data distributions.

Evaluation of near-duplicate methods uses labeled data to report precision, recall, and F1 scores, along with scalability metrics such as runtime and memory usage. Challenges include handling multilingual or highly paraphrastic content, maintaining performance on evolving corpora, and selecting representations and thresholds appropriate for the specific application, from web search to content recommendation and data deduplication.