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It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words. If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. [2]
Readability is the ease with which a reader can understand a written text.The concept exists in both natural language and programming languages though in different forms. In natural language, the readability of text depends on its content (the complexity of its vocabulary and syntax) and its presentation (such as typographic aspects that affect legibility, like font size, line height ...
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. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.
where f t,d is the raw count of a term in a document, i.e., the number of times that term t occurs in document d. Note the denominator is simply the total number of terms in document d (counting each occurrence of the same term separately). There are various other ways to define term frequency: [5]: 128 the raw count itself: tf(t,d) = f t,d
When all literals of a one-in-three 3-SAT formula are positive, the satisfiability problem is called one-in-three positive 3-SAT. One-in-three 3-SAT, together with its positive case, is listed as NP-complete problem "LO4" in the standard reference Computers and Intractability: A Guide to the Theory of NP-Completeness by Michael R. Garey and ...
Number blocks, which can be used for counting. Counting is the process of determining the number of elements of a finite set of objects; that is, determining the size of a set. . The traditional way of counting consists of continually increasing a (mental or spoken) counter by a unit for every element of the set, in some order, while marking (or displacing) those elements to avoid visiting the ...
A counting Bloom filter is a probabilistic data structure that is used to test whether the number of occurrences of a given element in a sequence exceeds a given threshold. As a generalized form of the Bloom filter, false positive matches are possible, but false negatives are not – in other words, a query returns either "possibly bigger or equal than the threshold" or "definitely smaller ...
All-equal and not-all-equal function: their values is 1 when the inputs do (not) all have the same value; Exact-count functions: their value is 1 on input vectors with k ones for a fixed k. One-hot or 1-in-n function: their value is 1 on input vectors with exactly one one; One-cold function: their value is 1 on input vectors with exactly one zero