Named entity recognizing method based on mixed model

Abstract

The invention relates to a named entity recognizing method based on a mixed model. The method comprises the following steps: pre-processing; by virtue of a self-adaptive selecting mode, in a hidden Markov model and a conditional random field model, selecting a model with a relatively high F value as a self-adaptive statistic recognizing model, initially recognizing the named entity for a recognized corpus to obtain an initial named entity recognizing result; constructing a basic dictionary formed by a knowledge base and a recognizing rule library; by virtue of the basic dictionary, performing secondary recognition on the initial named entity recognizing result by adopting the self-adaptive static recognizing model, and analyzing the F value of the secondary recognizing result, and updating the basic dictionary; and constructing the mixed model based on the basic dictionary and the self-adaptive statistic recognizing model, recognizing the to-be-recognized corpus to obtain a person name entity, a place name entity and an institute name entity in the to-be-recognized corpus, supplementing the recognizing result into the knowledge base, and updating the basic dictionary for recognition next time. According to the method provided by the invention, the recognizing accuracy and the recognizing recall rate are remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a named entity recognition method based on a mixed model. Background technique [0002] With the application of emerging networks such as the Internet, cloud computing, mobile media, and the Internet of Things, a large number of Web2.0 technologies that create content for users have been born, making Web applications enter the era of big data. A series of Internet services such as search engines, e-commerce, and social networking sites Derivative business developed rapidly. Big data in the modern era has four characteristics, namely large data volume, diverse data structures, fast data generation, and high commercial value. With large amounts of data, not all information is useful data. This leads to the coexistence of a large amount of invalid data and valuable data. Therefore, in the era of big data, how to find valuable data from huge data se...

AI Technical Summary

Problems solved by technology

This phenomenon of nesting among named entities greatly increases the difficulty of identifying complex named entities

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