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[3] [4] Although the two words precision and accuracy can be synonymous in colloquial use, they are deliberately contrasted in the context of the scientific method. The field of statistics , where the interpretation of measurements plays a central role, prefers to use the terms bias and variability instead of accuracy and precision: bias is the ...
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different ...
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
In computer science, software is typically divided into two types: high-level end-user applications software (such as word processors, databases, video games, etc.), and low-level systems software (such as operating systems, hardware drivers, firmwares, etc.). As such, high-level applications typically rely on low-level applications to function.
"the usefulness, accuracy, and correctness of data for its application" [10] Arguably, in all these cases, "data quality" is a comparison of the actual state of a particular set of data to a desired state, with the desired state being typically referred to as "fit for use," "to specification," "meeting consumer expectations," "free of defect ...
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).
Word recognition is measured as a matter of speed, such that a word with a high level of recognition is read faster than a novel one. [3] This manner of testing suggests that comprehension of the meaning of the words being read is not required, but rather the ability to recognize them in a way that allows proper pronunciation.
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus.