Sentence examples for frequencies of word from inspiring English sources

Taking into account all frequencies of word usage, we find for all three languages that the median shifts considerably towards positive values.

To calculate frequencies of word occurrences, we randomly selected 10,000 publicly available peer reviewed medical publications.

We created a series of statistical models predicting the frequencies of word of up to 4 nt in a genome.

One such system is the Protégé plug-in TerMine (Frantzi et al., 2000), which suggests terms by analyzing the frequencies of word tuples in a text for a given query.

Search for the term in Google's ngrams feature, which charts the frequencies of words and phrases in printed sources from 1800 onwards and you can see an Everest-like spike in usage, which reached a high point in 1920.

This data comes from Google's Ngram viewer, which charts the frequencies of words appearing in printed sources between 1800 and 2012.

While information theory has been most helpful in the design of more efficient telecommunication systems, it has also motivated linguistic studies of the relative frequencies of words, the length of words, and the speed of reading.

For the self-completeness of this paper, we also present our MapReduce algorithms to compute the frequencies of word-pair.

In this subsection, we study the structure to store the precompute results which are the frequencies of word-pair in tweets.

Now we give our MapReduce algorithm for computing the frequencies of word-pairs, as shown in Algorithms 1 and 2. We take the twitter collections and a pre-fixed index interval P as input in map Algorithm 1.

While we are unable to measure information perfectly, we can approximate its value given the frequencies of words and word sequences.