Named entity identification method and device

Abstract

The invention provides a named entity identification method and a named entity identification device capable of accurately identifying a named entity, in particular to a named entity in the field of E-business. The method comprises: acquiring a vector library; carrying out word segmentation on a training corpus text string to obtain a plurality of sample words; inquiring the vector library of each sample word sequentially to obtain a first feature vector which comprises a word vector and a word class vector corresponding to the same word as well as an entity marking vector corresponding to the last word of the sample word; taking all the first feature vectors integrally as an input quantity, and training a named entity identification model of a neutral network; carrying out word segmentation on a to-be-predicted text string to obtain a plurality of to-be-tested words; inquiring the vector library of each sample word sequentially to obtain a second feature vector which comprises a word vector and a word class vector corresponding to the same word as well as an entity marking vector corresponding to the last word of the sample word; respectively inputting the second feature vectors corresponding to all the to-be-tested words into the model, and outputting entity identifiers of the to-be-tested words.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular to a named entity recognition method and device. Background technique [0002] With the rapid development of Internet technology, information services are becoming more and more popular. Among them, the identification of named entities is an important basic work in information service application fields such as information extraction, question answering system, syntax analysis, machine translation, and Internet-oriented metadata annotation. Named entities (referred to as entities) refer to names of people, institutions, places, and all other entities that are identified by names. A wider range of named entities also includes numbers, dates, currencies, addresses, etc. [0003] In the prior art, there has been a technology of using neural network technology to train named entity recognition. The existing methods have at least the following disadvantages:...

AI Technical Summary

Problems solved by technology

The existing methods have at least the following disadvantages: (1) they mainly rely on the word itself as the input feature, the model features are single, and the front and back dependencies between entity tags are not directly introduced, resulting in low recognition accuracy, especially in When identifying named entities in the e-commerce field, the recognition is often inaccurate; (2) Since the initial value of the network is randomly generated, the final parameter optimization result may not be good enough, and the long training time leads to low development efficiency; (3) Insufficient Considering the distribution of training data leads to uneven fitting of the model to entities

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