Resume analysis method based on n-gram model

Abstract

The invention discloses a resume analysis method based on an n-gram model. The method comprises the steps that resume samples are collected in advance; commonly used field keywords are classified into different types, and a classification dictionary is formed; the n-gram model is used to conduct statistics of a transfer probability of converting each commonly used field keyword into each sample related word; a target keyword matched with the commonly used field keyword in a to-be-analyzed resume is searched; if the transfer probability corresponding to the target keyword is larger than a preset threshold, the transfer probability corresponding to each commonly used field keyword can be updated according to the target keyword; prefix labels and postfix labels are added to effective keywords in the to-be-analyzed resume; and text contents of the to-be-analyzed resume are extracted by segmentation and then output. According to the invention, the automatic resume analysis can be conducted based on the n-gram model and dictionary segmentation technologies; information extraction accuracy can be increased, and different document formats can be supported; and an abundant talent resource base can be provided for recruitment websites and company HR departments.

Description

technical field [0001] The invention relates to the technical field of computer science, in particular to a resume analysis method based on an n-gram model. Background technique [0002] With the rapid development of Internet technology, the network accommodates a large amount of raw data information of various types. In daily life, a resume is a very common and important text, which contains the author's basic situation, work experience and other information. Therefore, how to automatically, quickly and accurately extract useful information from massive resumes has become an urgent need for HR departments of major recruitment websites, companies and enterprises. [0003] Resume parsing is essentially an application of text information extraction. Currently, there are three main types of text information extraction models: dictionary-based extraction models, rule-based extraction models, and hidden Markov model-based extraction models. [0004] The existing resume parsing...

AI Technical Summary

Problems solved by technology

[0004] The existing resume parsing schemes that apply the above three text information extraction models mostly use simple keyword matching meth

Images