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Method, device and equipment for natural language processing

A natural language processing and algorithm technology, applied in natural language data processing, neural learning methods, electrical digital data processing, etc., can solve problems such as inability to perform self-learning, limited model capabilities, etc., to reduce the process of manual labeling and improve efficiency and accuracy, to achieve the effect of dynamic self-learning

Active Publication Date: 2022-02-15
BEIJING XIAOMI INTELLIGENT TECH CO LTD
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Problems solved by technology

However, the above models all have problems such as limited model capabilities and inability to perform self-learning.

Method used

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  • Method, device and equipment for natural language processing
  • Method, device and equipment for natural language processing
  • Method, device and equipment for natural language processing

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Embodiment Construction

[0058] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

[0059]The terminology used in the present disclosure is for the purpose of describing particular embodiments only, and is not intended to limit the present disclosure. As used in this disclosure and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood ...

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Abstract

The present disclosure is a method, device and equipment for natural language processing, which are applied to dialogue robots in human-machine dialogue systems, wherein the method includes: determining the bidirectional long-short-term memory network algorithm and conditional random field algorithm BiLSTM-CRF model used The word slot recognition result output after the word slot recognition is performed on the dialogue data input by the user; the feedback information is determined based on the word slot recognition result and the user's feedback on the word slot recognition result; CRF model for reinforcement learning. This embodiment can realize the dynamic self-learning of the model, so as to reduce the process of manual labeling and improve the efficiency and accuracy of word slot recognition.

Description

technical field [0001] The present disclosure relates to the technical field of human-computer dialogue, and in particular to methods, devices and equipment for natural language processing. Background technique [0002] Natural language processing is a science that combines linguistics, computer science, and mathematics to study theories and methods for effective communication between humans and computers using natural language. In natural language processing, the sequence annotation model is a commonly used model and is widely used in related fields such as text processing. [0003] Current popular methods to solve the sequence labeling problem include Hidden Markov Models (HMMs), Conditional Random Fields (CRFs) and Recurrent Neural Networks (RNNs). However, the above models all have problems such as limited model capabilities and inability to perform self-learning. Contents of the invention [0004] In order to overcome the problems existing in related technologies, t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/332G06F16/33G06F40/279
CPCG06F16/3329G06F16/3344G06F40/35G06F40/295G06N3/08G06N3/006G06N3/047G06N3/044G06N20/00G06F16/33G06F16/90332G06F40/30
Inventor 钱庄
Owner BEIJING XIAOMI INTELLIGENT TECH CO LTD
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