By Rafael E. Banchs

Textual content Mining with MATLAB offers a entire advent to textual content mining utilizing MATLAB. It’s designed to assist textual content mining practitioners, in addition to people with little-to-no adventure with textual content mining usually, familiarize themselves with MATLAB and its complicated functions. the 1st half offers an creation to simple techniques for dealing with and working with textual content strings. Then, it stories significant mathematical modeling methods. Statistical and geometrical versions also are defined in addition to major dimensionality relief equipment. eventually, it offers a few particular functions comparable to record clustering, type, seek and terminology extraction. All descriptions offered are supported with functional examples which are totally reproducible. extra interpreting, in addition to extra routines and initiatives, are proposed on the finish of every bankruptcy for these readers attracted to engaging in extra experimentation.

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In this sense, quantifiers can be turned into lazy quantifiers by appending to them a question mark symbol. The following two examples illustrate the difference between using a greedy or a lazy quantifier. "','match') ð3:15Þ ans = '"sample 1"' '"sample a"' '"sample20"' '"sample 321"' Another important group of operators are the logical operators. In the remaining of this section we will introduce the basic logical operators, and next section will be specifically devoted to conditional operators.

The following script provides a means for both separating sequences of alphanumeric characters by inserting white spaces in between sequences of alphanumeric and non-alphanumeric characters, and eliminating redundant white spaces: temp = regexprep(string,'([^\w\s]+)',' $1 '); % inserts white spaces... % before and after non-alphanumeric character sequences temp = regexprep(temp,' +',' '); % removes redundant spaces ð4:27Þ string = strtrim(temp); % removes any possible initial and final spaces The operation of the proposed script is as follows.

Another important observation is that, in the first case, when strcat is used, trailing blanks are removed from the input strings. 35a) how the words example and on have been merged together into the single token exampleon. If preserving trailing blanks is a requirement, a different concatenation strategy, as will be discussed below, must be considered. In the second case, on the other hand, elements of the input cell array are mapped one-cell-per-row into a two-dimensional character array. Notice that, in this case, individual input strings are padded with blanks so each row in the array has the same number of characters.

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Text Mining with MATLAB® [without chapter 1] by Rafael E. Banchs
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