Sequence labeling method and system, computer equipment and computer readable storage medium

A sequence labeling and sequence technology, applied in the field of sequence labeling methods, systems, computer equipment and computer-readable storage media, can solve problems such as limited linear assumptions and inaccuracy, and achieve the effect of sequence labeling accuracy
CN110866115AActive Publication Date: 2020-03-06PING AN TECH (SHENZHEN) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
PING AN TECH (SHENZHEN) CO LTD
Publication Date
2020-03-06

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Abstract

The embodiment of the invention discloses a sequence labeling method, which comprises the following steps: obtaining a training sample set, wherein the training sample set comprises a plurality of pieces of training sample data, and each piece of training sample data comprises an input text sequence and a label corresponding to the input text sequence; preprocessing each piece of training sample data to obtain vector data corresponding to each piece of training sample data; inputting vector data corresponding to each piece of training sample data into a first-order hidden Markov model to construct a feature vector matched with each piece of training sample data; inputting the feature vectors corresponding to the sample data into a neural network model for training to generate a sequence labeling model; and inputting the to-be-labeled sequence into the sequence labeling model to obtain a target label sequence corresponding to the to-be-labeled sequence. The embodiment of the invention further discloses a sequence labeling system, computer equipment and a readable storage medium. The embodiment of the invention has the beneficial effect that the sequence annotation is more accurate.
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Description

technical field

[0001] The embodiments of the present invention relate to the technical field of natural speech processing, and in particular to a sequence tagging method, system, computer equipment and computer-readable storage medium. f(x; θ) = argmaxF(x, y; θ)

[0002] technical background

[0003] At present, sequence tagging is a basic and important problem in natural language processing, which includes tasks such as word segmentation, part-of-speech tagging, named entity recognition, and relationship extraction. The sequence labeling problem is also a classic problem in structure learning, which is achieved by finding to get the label y for the sequence x.

[0004] Structural support vector machine is a classic method of structural learning. The goal of structural support vector machine is not only to maximize the score of the correct label sequence, but also to maximize the score between the correct label sequence and the score of the nearest incorrect label sequenc...

Claims

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