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  2. Normalized Google distance - Wikipedia

    en.wikipedia.org/wiki/Normalized_Google_distance

    The normalized Google distance (NGD) is a semantic similarity measure derived from the number of hits returned by the Google search engine for a given set of keywords. [1] Keywords with the same or similar meanings in a natural language sense tend to be "close" in units of normalized Google distance, while words with dissimilar meanings tend to ...

  3. Normalized compression distance - Wikipedia

    en.wikipedia.org/.../Normalized_compression_distance

    Using code-word lengths obtained from the page-hit counts returned by Google from the web, we obtain a semantic distance using the NCD formula and viewing Google as a compressor useful for data mining, text comprehension, classification, and translation. The associated NCD, called the normalized Google distance (NGD) can be rewritten as

  4. Information distance - Wikipedia

    en.wikipedia.org/wiki/Information_Distance

    We need outside information about what the name means. Using a data base (such as the internet) and a means to search the database (such as a search engine like Google) provides this information. Every search engine on a data base that provides aggregate page counts can be used in the normalized Google distance (NGD). A python package for ...

  5. Semantic similarity - Wikipedia

    en.wikipedia.org/wiki/Semantic_similarity

    NGD (normalized Google distance): (+) large vocab, because it uses any search engine (like Google); (−) can measure relatedness between whole sentences or documents but the larger the sentence or document, the more ingenuity is required (Cilibrasi & Vitanyi, 2007). [46]

  6. Jaccard index - Wikipedia

    en.wikipedia.org/wiki/Jaccard_index

    Jaccard distance is commonly used to calculate an n × n matrix for clustering and multidimensional scaling of n sample sets. This distance is a metric on the collection of all finite sets. [8] [9] [10] There is also a version of the Jaccard distance for measures, including probability measures.

  7. Paul Vitányi - Wikipedia

    en.wikipedia.org/wiki/Paul_Vitányi

    [5] [6] [7] As of 2020 his work on normalized compression distance was used in 15 US patents and on normalized Google distance in 10 US patents. Together with Ming Li he pioneered theory and applications of Kolmogorov complexity. [8]

  8. Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Levenshtein_distance

    Edit distance matrix for two words using cost of substitution as 1 and cost of deletion or insertion as 0.5. For example, the Levenshtein distance between "kitten" and "sitting" is 3, since the following 3 edits change one into the other, and there is no way to do it with fewer than 3 edits: kitten → sitten (substitution of "s" for "k"),

  9. Talk:Normalized Google distance - Wikipedia

    en.wikipedia.org/.../Talk:Normalized_Google_distance

    Talk: Normalized Google distance. Add languages. Page contents not supported in other languages. Article; ... Page information; Get shortened URL; Download QR code;