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Multi-round conversation semantic analysis method and system based on long-term and short-term memory network

A long-short-term memory and semantic analysis technology, applied in the computer field, can solve problems such as poor prediction ability of new dialogues and ambiguity of multiple rounds of dialogues, and achieve the effect of resolving ambiguities

Active Publication Date: 2020-01-17
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the purpose of the embodiments of the present invention is to provide a long-short-term memory network-based multi-round dialog semantic analysis method, system, computer equipment, and computer-readable storage medium. The present invention can accurately understand dialog information and solve multiple problems. The ambiguity of dialogue rounds and poor predictive ability for new dialogues

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  • Multi-round conversation semantic analysis method and system based on long-term and short-term memory network
  • Multi-round conversation semantic analysis method and system based on long-term and short-term memory network
  • Multi-round conversation semantic analysis method and system based on long-term and short-term memory network

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Embodiment 1

[0063] refer to figure 1 , shows a flow chart of the steps of the long-short-term memory network-based multi-round dialogue semantic analysis method according to Embodiment 1 of the present invention. It can be understood that the flowchart in this method embodiment is not used to limit the sequence of execution steps. details as follows:

[0064] Step S100, acquiring current session information provided by the client.

[0065] Step S102, generating a current dialogue representative vector according to the current dialogue information.

[0066] In an exemplary embodiment, such as figure 2 As shown, step S102 may include steps S102A-S1021.

[0067] Step S102A, extracting multiple keywords from the current dialogue information.

[0068] Exemplarily, multiple keywords are extracted from the current dialogue information according to a preset keyword template.

[0069] In step S102B, a plurality of corresponding substructures are obtained according to the plurality of keywor...

Embodiment 2

[0154] read on Figure 5 , shows a schematic diagram of the program modules of Embodiment 2 of the long-short-term memory network-based multi-round dialogue semantic analysis system 20 of the present invention. In this embodiment, the long-short-term memory network-based multi-round dialogue semantic analysis system 20 may include or be divided into one or more program modules, one or more program modules are stored in a storage medium, and are controlled by one or more processors, to complete the present invention, and can realize the above-mentioned multi-round dialogue semantic analysis method based on long-short-term memory network. The program module referred to in the embodiment of the present invention refers to a series of computer program instruction segments capable of completing specific functions, and is more suitable than the program itself to describe the execution process of the multi-round dialogue semantic analysis system 20 based on the long-short-term memory...

Embodiment 3

[0181] refer to Figure 6 , is a schematic diagram of the hardware architecture of the computer device according to Embodiment 3 of the present invention. In this embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and / or information processing according to preset or stored instructions. The computer device 2 may be a rack server, a blade server, a tower server or a cabinet server (including an independent server, or a server cluster composed of multiple servers) and the like. As shown in the figure, the computer device 2 at least includes, but is not limited to, a memory 21, a processor 22, a network interface 23, and a multi-round dialogue semantic analysis system 20 based on a long-short-term memory network that can communicate with each other through a system bus. in:

[0182] In this embodiment, the memory 21 includes at least one type of computer-readable storage medium, and the readable storage medium includes flash ...

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Abstract

The embodiment of the invention provides a multi-round conversation semantic analysis method based on a long-term and short-term memory network. The method comprises the steps that current conversation information is acquired; generating a current conversation representative vector according to the current conversation information; generating a knowledge code representation vector according to thecurrent conversation representation vector and a plurality of historical conversation code vectors acquired in advance; inputting the knowledge code representation vector and the word vector of eachsegmented word in the current conversation information into a first long-term and short-term memory model to obtain a prediction sequence label of the current conversation information; and obtaining corresponding semantic information according to the prediction sequence label, and executing corresponding operation according to the semantic information. The embodiment of the invention provides a multi-round conversation semantic analysis method and system based on a long-term and short-term memory network, computer equipment and a computer readable storage medium. According to the embodiment ofthe invention, the conversation information can be accurately understood, and the problems of ambiguity of multiple rounds of conversations and poor prediction capability for new conversations can besolved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a long-short-term memory network-based multi-round dialogue semantic analysis method, system, computer equipment, and computer-readable storage medium. Background technique [0002] With the large-scale popularization and application of artificial intelligence, human-computer dialogue, as an indispensable direction in the field of artificial intelligence, has received more and more attention and attention. [0003] Natural language understanding (NLU, natural language understanding) is a very important part of the dialogue system, especially in the task-based multi-turn dialogue system. NLU mainly performs semantic understanding by labeling dialogues. For the situation of multiple rounds of dialogue, the traditional dialogue system handles each round of dialogue separately, ignoring the historical dialogue information. Firstly, this system has the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06F16/35G06F40/289G06F40/30G06N3/04G06N3/08
CPCG06F16/3329G06F16/35G06N3/08G06N3/044G06N3/045Y02D10/00
Inventor 金戈徐亮
Owner PING AN TECH (SHENZHEN) CO LTD
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