Phrase Mining

• URL: http://arxiv.org/abs/2206.13748v1
• Date: Tue, 28 Jun 2022 04:11:31 GMT
• Title: Phrase Mining
• Authors: Ellie Small, Javier Cabrera
• Abstract summary: We present a method that eliminates double-counting without the need to identify 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. An R package called phm has been developed that implements this method.
• Score: 0.8223798883838329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting frequent words from a collection of texts is performed on a great scale in many subjects. Extracting phrases, on the other hand, is not commonly done due to inherent complications when extracting phrases, the most significant complication being that of double-counting, where words or phrases are counted when they appear inside longer phrases that themselves are also counted. 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 a method that eliminates double-counting without the need to identify 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 can identify such principal phrases independently without human input, and enables their extraction from any texts. An R package called phm has been developed that implements this method.

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