A method and system for entity recognition based on entity extension
An entity and entity word technology, applied in the field of data recognition, can solve the problems of strong dependence on labeled samples and low recognition accuracy.
Active Publication Date: 2021-09-10
杭州费尔斯通科技有限公司
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[0004] The embodiment of the present application provides a method and system for entity recognition based on entity extension to at least solve the problems of strong dependence on labeled samples and low recognition accuracy in related technologies
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[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0051] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios. In addition, it can also be understood that although such development efforts may be complex and lengthy, for those of ...
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The present application relates to a method and system for entity recognition based on entity expansion, wherein the method includes: segmenting unlabeled corpus sentences through various phrase mining methods to obtain a concept word candidate set, wherein the concept word candidate set includes several Concept words, train concept words through word embedding algorithm, get word vectors of concept words, calculate text features of concept words according to word vectors, set concept words as positive and negative samples according to domain entity word list, and set positive and negative samples according to positive and negative samples and text Features to train the classification model, get the confidence of concept words, and re-segment according to the confidence, and get entity words based on entity expansion. Through this application, the problems of strong dependence on labeled samples and low recognition accuracy in entity recognition are solved. The supervised learning using the domain entity word list is realized, the entity recognition result of the unlabeled corpus is obtained, and the effect of expanding the domain entity word list is achieved at the same time.
Description
technical field [0001] The present application relates to the field of data identification, in particular to an entity extension-based entity identification method and system. Background technique [0002] In the application scenarios of text information extraction, due to the diverse and detailed scenarios, sample labeling becomes an important part of the text information extraction process. Industrial applications are faced with the current situation of lack of labeled samples and high cost of sample labeling. The current text information extraction Among the methods, the method based on model training requires a large number of labeled samples. Although some deep models show a trend of higher and higher accuracy and fewer and fewer labeled samples, they still need a certain amount of labeled samples to be trained. The available models cannot work until the marked samples are obtained. Such a process is equivalent to transferring the development cost to the sample labeling...
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IPC IPC(8): G06F40/295G06F40/289G06F40/284G06F40/211G06K9/62G06N3/04
CPCG06F40/295G06F40/289G06F40/211G06F40/284G06N3/04G06F18/2411G06F18/24323
Inventor 韩瑞峰金霞杨红飞程东
Owner 杭州费尔斯通科技有限公司