Both methods have a wide range of parameters and methods of comparison, some using highly sophisticated statistics. The other constructs a vector of words which characterize the document and performs its comparison on the vectors. One divides the text into small, sometimes overlapping groups of sequential words called shingles, measures similarity by the proportion of identical shingles found in pairs of documents. There are many approaches to similarity estimation but they fall into two groups. Synonyms are another complication – multiple words may describe the same thing. It is often the case that using a different text extractor on the same document will give different results.Īssuming perfect text extraction, is the proportion of identical words in two documents a measure of similarity? Unfortunately not – two documents with the same words in different orders will appear identical by this measure. Extraction of words from text documents is not a straightforward matter, although a multitude of text extraction components exist, as text extraction forms a key part of the process of building search engine indexes. Hence, statements such as “40% of all text documents are similar” are highly qualified.įor text documents, the task is much more difficult, as the order of words is significant, as well as their meanings. The variety of algorithms and the statistical nature of the analysis means that similarity measurements are not absolute – two documents measured as 75% similar using one algorithm or set of parameters will not have the same similarity using a different algorithm or parameter set. Existing methods of similarity estimation use statistical analysis of groups of words as a robust substitute for analysis of meaning. Whilst identical documents are easily defined and identified (via a variety of checksum algorithms which produce a string based on the byte content of the file), the definition of similarity depends on a deeper understanding of the document and its meaning. Document similarity is often estimated by a percentage, where 100% is identical. Near-duplicate documents have similar, but not necessarily identical, content.
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