  Extracting frequent words from a collection of texts is commonly performed in
many subjects. However, as useful as it is to obtain a collection of commonly
occurring words from texts, there is a need for more specific information to be
obtained from texts in the form of most commonly occurring phrases. Despite
this need, extracting frequent phrases is not commonly done due to inherent
complications, the most significant being double-counting. Double-counting
occurs when words or phrases are counted when they appear inside longer phrases
that themselves are also counted, resulting in a selection of mostly
meaningless phrases that are frequent only because they occur inside frequent
super phrases. Several papers have been written on phrase mining that describe
solutions to this issue; however, they either require a list of so-called
quality phrases to be available to the extracting process, or they require
human interaction to identify those quality phrases during the process. We
present here a method that eliminates double-counting via a unique
rectification process that does not require lists of quality phrases. In the
context of a set of texts, we define a principal phrase as a phrase that does
not cross punctuation marks, does not start with a stop word, with the
exception of the stop words "not" and "no", does not end with a stop word, is
frequent within those texts without being double counted, and is meaningful to
the user. Our method identifies such principal phrases independently without
human input, and enables their extraction from any texts within a reasonable
amount of time.
