Training method and device for multi-category entity recognition model
An entity recognition and training method technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of high maintenance cost, high error rate, low operation efficiency, etc., to save costs, improve accuracy and efficiency Effect
Active Publication Date: 2021-01-29
ZHIZHESIHAIBEIJINGTECH CO LTD
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[0004] In view of this, the present disclosure provides a multi-category entity recognition model training method and device, which can effectively solve the problems of high maintenance cost, high error rate and low operating efficiency of multi-category entity recognition methods in the prior art
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[0051] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
[0052]It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present application, relative terms such as "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations, any such actual relationship or order exists. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements ...
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The present disclosure provides a training method and device for a multi-category entity recognition model. The method includes: generating a first entity recognition model; using N types of single-category corpus to train the first entity recognition model respectively to obtain N single-category Entity recognition model, wherein N is an integer greater than or equal to 2; use N single-category entity recognition models to identify mixed corpus including multi-category entities, and obtain marked N-category mixed corpus; use the marked N-category mixed corpus to An entity recognition model is trained to obtain N-category entity recognition models. The training method and device for the multi-category entity recognition model provided by the present disclosure can effectively solve the problems of high maintenance cost, high error rate and low operation efficiency of the multi-category entity recognition method in the prior art.
Description
technical field [0001] The present disclosure relates to the technical field of natural language processing, and in particular to a training method and device for a multi-category entity recognition model. Background technique [0002] In the field of natural language processing technology, Named Entity Recognition (NER for short) refers to extracting entities with specific meaning or strong referentiality from unstructured input text, and usually classifying entities into names and place names. , organization name, date and time, and other proper nouns. [0003] The current multi-category entity recognition mostly adopts dictionary matching method or context-aware method. However, the dictionary matching method relies on the word segmentation effect and thesaurus data, which has poor accuracy and high maintenance costs; while the context-aware method needs to perform word segmentation and category judgment according to the context, which is inefficient and has a high error...
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IPC IPC(8): G06F40/295G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045
Inventor 李飞阳薛姣胡鸣鹤孙付伟
Owner ZHIZHESIHAIBEIJINGTECH CO LTD