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Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
This is a comparison of English dictionaries, which are dictionaries about the language of English.The dictionaries listed here are categorized into "full-size" dictionaries (which extensively cover the language, and are targeted to native speakers), "collegiate" (which are smaller, and often contain other biographical or geographical information useful to college students), and "learner's ...
Lesk algorithm is a classical algorithm for word sense disambiguation introduced by Michael E. Lesk in 1986. [1] It operates on the premise that words within a given context are likely to share a common meaning.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
This does not mean using fewer words is always better; rather, when considering equivalent expressions, choose the more concise. Vigorous writing is concise. A sentence should contain no unnecessary words, a paragraph no unnecessary sentences, for the same reason that a drawing should have no unnecessary lines and a machine no unnecessary parts.
The timing and accuracy of word recognition relies on where in the word the eye is currently fixating. Recognition is fastest and most accurate when fixating in the middle of the word. This is due to a decrease in visual acuity that results as letters are situated farther from the fixated location and become harder to see.
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More particularly, in assessing the merits of an argument, a measurement, or a report, an observer or assessor falls prey to precision bias when they believe that greater precision implies greater accuracy (i.e., that simply because a statement is precise, it is also true); the observer or assessor are said to provide false precision. [3] [4]