Natural language processing method, device and equipment

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

Active Publication Date: 2019-11-05
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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  • Natural language processing method, device and equipment
  • Natural language processing method, device and equipment
  • Natural language processing method, device and equipment

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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 invention discloses a natural language processing method, device and equipment. The method is applied to a dialogue robot in a man-machine dialogue system. The method comprises the following steps: determining a word slot identification result output after performing word slot identification on dialogue data input by a user by using a bidirectional long short-term memory network algorithm anda conditional random field algorithm BiLSTM-CRF model; determining feedback information based on the word slot identification result and feedback of the user to the word slot identification result; and carrying out reinforcement learning on the BiLSTM-CRF model according to the feedback information. According to the embodiment, dynamic self-learning of the model can be realized, so that the manualannotation process is reduced, and the word slot recognition efficiency and accuracy are improved.

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 Applications(China)
IPC IPC(8): G06F16/332G06F16/33G06F17/27
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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