The problem they're trying to solve is to find near duplicate web pages, which may have different layout, different branding and interspersed adverts.
They do this by using the next few words after a stop word as a signature. For instance:
From Greg Linden's blog
The paper gives an example of a generating a spot signature where a piece of text like "at a rally to kick off a weeklong campaign" produces two spots: "a:rally:kick" and "a:weeklong:campaign".
From the paper: SpotSigs: Robust and Efficient Near Duplicate Detection in Large Web Collections (PDF)
The contributions of SpotSigs are twofold:
- by combining stopword antecedents with short chains of adjacent content terms, we create robust document signatures with a natural ability to filter out noisy components of Web pages that would otherwise distract pure n-gram-based approaches such as Shingling;
- we provide an exact and efficient, self- tuning matching algorithm that exploits a novel combination of collection partitioning and inverted index pruning for high-dimensional similarity search. Experiments confirm an increase in combined precision and recall of more than 24 percent over state-of-the-art approaches such as Shingling or I-Match and up to a factor of 3 faster execution times than Locality Sensitive Hashing (LSH), over a demonstrative "Gold Set" of manually assessed near-duplicate news articles as well as the TREC WT10g Web collection.
Makes for an interesting read.
In reply to [OT] SpotSigs: Robust and Efficient Near Duplicate Detection in Large Web Collections by clinton
| For: | Use: | ||
| & | & | ||
| < | < | ||
| > | > | ||
| [ | [ | ||
| ] | ] |