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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 ...
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
Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content [citation needed] as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of ...
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 ...
Download QR code; Print/export Download as PDF; Printable version; In other projects ... Normalized compression distance; Normalized Google distance; O. Optimal ...
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
We can rewrite this definition in terms that explicitly highlight the information content of this metric. The set of all partitions of a set form a compact lattice where the partial order induces two operations, the meet and the join , where the maximum ¯ is the partition with only one block, i.e., all elements grouped together, and the minimum is ¯, the partition consisting of all elements ...
This distance is robust to noise, since the distance between two points depends on all possible paths of length between the points. From a machine learning point of view, the distance takes into account all evidences linking x i {\displaystyle x_{i}} to x j {\displaystyle x_{j}} , allowing us to conclude that this distance is appropriate for ...