Principal Phrase Mining

TL;DR

This paper introduces a novel method for extracting meaningful, non-double-counted phrases from texts without requiring human input or quality phrase lists, addressing a key challenge in phrase mining.

Contribution

The proposed approach automatically identifies principal phrases by eliminating double-counting, enhancing phrase extraction accuracy without human intervention or pre-existing quality phrase lists.

Findings

Effectively eliminates double-counting in phrase extraction

Identifies meaningful principal phrases independently

Operates efficiently on various text collections

Abstract

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.