POI (point of interest) Chinese text categorizing method based on local random word density model

Abstract

The invention discloses a POI (point of interest) Chinese text categorizing method based on a local random word density model. The method includes: judging whether the subject of a text is related to POI or not, and using a modified word concentration, dispersion and frequency method to screen out feature words to build a feature space; performing local area dividing according to the similarity degree of the text and POI category, converting the text into feature vectors in each local area through feature mapping matrixes, and using SVM to perform POI text categorization. The method is good in execution efficiency and categorizing coverage and accuracy. The method is planned to be combined with large knowledge libraries of Hownet so as to capture semantic concepts of low-frequency words and unseen words and further increase the recognition capability of POI text differences, and the problem that good categorization performance cannot be obtained by existing conventional feature evaluation functions and text dimensionality reduction methods is solved.

Description

technical field [0001] The invention belongs to the technical field of mass interest point text classification, and in particular relates to a method for classifying Chinese text based on a local random vocabulary density model POI. Background technique [0002] The traditional method of collecting massive Point of Interest (POI) is the field survey of surveying and mapping departments at all levels. The data collected by this method has high precision, but the collection efficiency is low, the information update speed is slow, and the coverage is often insufficient. In addition, there are two methods of POI collection: professional company production and VGI public collection (such as Go2Map and Locationary). The former still has not solved the problem of requiring a large number of manual POI labels, resulting in insufficient data depth and difficulty in updating; the latter has a large number of multiple sources. POI data management, platform heterogeneity, service popul...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the embodiments of the present invention is to provide a method for classifying Chinese text based on the local stochastic vocabulary density model POI, which aims to solve the problem that the existing conventional feature evaluation function and text dimensionality reduction method cannot achieve better classification performance

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