Duplicate detection Sample Clauses

Duplicate detection. The Web contains many duplicate pages, texts and their parts. Ignoring this phenomenon and including duplicate documents (or their parts) in the corpus could have negative effect on training the MT system. Duplicate detection involves identification of documents (or their parts) already appearing in the corpus and their elimination. In the area of web page crawling, the attention is focused on detection of near duplicate pages. Two pages with the same main content can differ in other parts (boilerplate) and therefore duplicate detection algorithms would fail in identifying them as full duplicates.
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Duplicate detection. (Near) duplicate detection is a difficult task because, generally, it is a quadratic problem: each new candidate document before being added to the corpus it must be checked against all other documents appearing in the corpus (e.g. by document similarity measures). Although such methods are quite accurate, the speed becomes a serious problem in large document collections. Therefore, several authors proposed methods that reduce the time complexity to sub-quadratic: Shingling (Xxxxxx, 1997), I-Match (Xxxxxxxxx et al., 2002), Locality Sensitive Hashing (Xxxxxx et al., 1999) and SpotSigs (Xxxxxxxx et. al., 2008). SpotSigs, which specifically targets duplicate detection for web crawling, represents each web page as a set of spot signatures. A spot signature is a chain of words that follow frequent words as these are attested in a corpus. These signatures are rarely present in advertisements and navigational components of web pages. Thus, the signatures are built from portions of pages with ―real‖ content. Then, SpotSigs adopts an efficient and self-tuning matching algorithm based on Jaccard similarity of sets of spot signatures, in order to derive an optimal partitioning of the web page collection into buckets of potentially matching documents, and thus to reduce the problem of identifying duplicates into a sub-quadratic one. Xxxxxxxx et al. (2008) report that SpotSigs outperformed Shingling and I-Match algorithms in terms of recall and precision, and Locality Sensitive Hashing in efficiency over the TREC WT10g Web collection.
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