Efficient and accurate near-duplicate detection is a trending topic of research. Complications arise from the great time and space complexities of existing algorithms. This study proposes a novel pruning strategy to improve pairwise comparison-based near-duplicate detection methods. After parsing the documents into punctuation-delimited blocks called chunks, it decides between the categories of “near duplicate,” “non-duplicate” or “suspicious” by applying certain filtering rules. This early decision makes it possible to disregard many of the non-necessary computations—on average 92.95% of them. Then, for the suspicious pairs, common chunks and short chunks are removed and the remaining subsets are reserved for near-duplicate detection. Size of the remaining subsets is on average 4.42% of the original corpus size. Evaluation results show that near-duplicate detection with the proposed strategy in its best configuration (CHT = 8, τ = 0.1) has F-measure = 87.22% (precision = 86.91% and recall = 87.54%). Its F-measure is comparable with the SpotSig method with less execution time. In addition, applying the proposed strategy in a near-duplicate detection process eliminates the need for preprocessing. It is also tunable to achieve the intended levels of near duplication and noise suppression.
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Distribution frequency of the number of chunks in the documents for different CHTs:
See Table 5.
3.1 Setting the similarity threshold
Results of the experiments with the aim of finding the best value for the similarity threshold are provided below. In line with previous studies [32, 37], the results show that selecting τ = 0.6 (between the two turning points of 0.5 and 0.7) would lead to an acceptable performance (Fig. 15).
3.2 k-Shingling on m random Shingles
Several experiments were conducted by having k from 1 to 3 and m from 200 to 500. The results are provided in Fig. 16. As shown by the results, the best F-measure is achieved by having (k = 1 and m = 200) or (k = 1 and m = 300) or (k = 1 and m = 400) or (k = 1 and m = 500). Selecting each of the mentioned settings would result in F-measure = 82.11%.
3.3 k-Shingling on m most frequent Shingles
Several experiments were conducted by having k from 1 to 3 and m from 200 to 500. The results are provided in Fig. 17. As shown by the results, the best F-measure is achieved by having (k = 1 and m = 200) or (k = 1 and m = 300) or (k = 1 and m = 400) or (k = 1 and m = 500). Selecting each of the mentioned settings would result in F-measure = 66.86%.
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Hassanian-esfahani, R., Kargar, Mj. A pruning strategy to improve pairwise comparison-based near-duplicate detection. Knowl Inf Syst 61, 931–963 (2019). https://doi.org/10.1007/s10115-018-1299-2
- Near-duplicate detection
- Pruning strategy