sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition
The invention provides a sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition. The scheme is realized from six aspectsof deep learning environment construction, generative adversarial network framework construction, que...
Gespeichert in:
Hauptverfasser: | , , , , , , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | LIU SHOUWEN CHEN SHASHA WEN BINGBING LIU YI LI FAN LIAO YUKUN SHANGGUAN ZHAOHUI YING JUNYU |
description | The invention provides a sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition. The scheme is realized from six aspectsof deep learning environment construction, generative adversarial network framework construction, questioning sample meta-semantic decomposition, response sample semantic cutting, response sample meta-semantic decomposition and response sample set expansion. Through the steps of establishing a deep learning training environment and a generative adversarial network framework, extracting meta-semantics of response samples of questioning samples and the like, enhancement of a generative model training sample set is finally realized.
本发明提供一种基于元语义分解的电力客服留言生成模型样本处理系统及方法。本发明从"深度学习环境搭建、生成对抗网络框架搭建、提问样本元语义分解、应答样本语义切割、应答样本元语义分解、应答样本集扩充"六个方面来实现此方案,通过搭建深度学习训练环境与生成对抗网络框架,接着通过对提问样本的应答样本的元语义提取等步骤,最终实现生成模型训练样本集的增强。 |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN110929085A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN110929085A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN110929085A3</originalsourceid><addsrcrecordid>eNqNjEEKwjAQRbtxIeodxgMUWkWwSymKK1fuy5h8a6DJhExUPID3toIHcPXfh8ebFm9lHwdQTGKg6kJP-tIMTxwseeSbWLpKIgwwOTlDUZ5IZO6axY-gSA9nMKqq3IN6BCTOTgJ5sRjowgpL34vMpcJzyGPGwoiPou6rzovJlQfF4rezYnnYn9tjiSgdNLIZq7lrT3VdNaum2m5263-cD4qPTE8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition</title><source>esp@cenet</source><creator>LIU SHOUWEN ; CHEN SHASHA ; WEN BINGBING ; LIU YI ; LI FAN ; LIAO YUKUN ; SHANGGUAN ZHAOHUI ; YING JUNYU</creator><creatorcontrib>LIU SHOUWEN ; CHEN SHASHA ; WEN BINGBING ; LIU YI ; LI FAN ; LIAO YUKUN ; SHANGGUAN ZHAOHUI ; YING JUNYU</creatorcontrib><description>The invention provides a sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition. The scheme is realized from six aspectsof deep learning environment construction, generative adversarial network framework construction, questioning sample meta-semantic decomposition, response sample semantic cutting, response sample meta-semantic decomposition and response sample set expansion. Through the steps of establishing a deep learning training environment and a generative adversarial network framework, extracting meta-semantics of response samples of questioning samples and the like, enhancement of a generative model training sample set is finally realized.
本发明提供一种基于元语义分解的电力客服留言生成模型样本处理系统及方法。本发明从"深度学习环境搭建、生成对抗网络框架搭建、提问样本元语义分解、应答样本语义切割、应答样本元语义分解、应答样本集扩充"六个方面来实现此方案,通过搭建深度学习训练环境与生成对抗网络框架,接着通过对提问样本的应答样本的元语义提取等步骤,最终实现生成模型训练样本集的增强。</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200327&DB=EPODOC&CC=CN&NR=110929085A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200327&DB=EPODOC&CC=CN&NR=110929085A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LIU SHOUWEN</creatorcontrib><creatorcontrib>CHEN SHASHA</creatorcontrib><creatorcontrib>WEN BINGBING</creatorcontrib><creatorcontrib>LIU YI</creatorcontrib><creatorcontrib>LI FAN</creatorcontrib><creatorcontrib>LIAO YUKUN</creatorcontrib><creatorcontrib>SHANGGUAN ZHAOHUI</creatorcontrib><creatorcontrib>YING JUNYU</creatorcontrib><title>sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition</title><description>The invention provides a sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition. The scheme is realized from six aspectsof deep learning environment construction, generative adversarial network framework construction, questioning sample meta-semantic decomposition, response sample semantic cutting, response sample meta-semantic decomposition and response sample set expansion. Through the steps of establishing a deep learning training environment and a generative adversarial network framework, extracting meta-semantics of response samples of questioning samples and the like, enhancement of a generative model training sample set is finally realized.
本发明提供一种基于元语义分解的电力客服留言生成模型样本处理系统及方法。本发明从"深度学习环境搭建、生成对抗网络框架搭建、提问样本元语义分解、应答样本语义切割、应答样本元语义分解、应答样本集扩充"六个方面来实现此方案,通过搭建深度学习训练环境与生成对抗网络框架,接着通过对提问样本的应答样本的元语义提取等步骤,最终实现生成模型训练样本集的增强。</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjEEKwjAQRbtxIeodxgMUWkWwSymKK1fuy5h8a6DJhExUPID3toIHcPXfh8ebFm9lHwdQTGKg6kJP-tIMTxwseeSbWLpKIgwwOTlDUZ5IZO6axY-gSA9nMKqq3IN6BCTOTgJ5sRjowgpL34vMpcJzyGPGwoiPou6rzovJlQfF4rezYnnYn9tjiSgdNLIZq7lrT3VdNaum2m5263-cD4qPTE8</recordid><startdate>20200327</startdate><enddate>20200327</enddate><creator>LIU SHOUWEN</creator><creator>CHEN SHASHA</creator><creator>WEN BINGBING</creator><creator>LIU YI</creator><creator>LI FAN</creator><creator>LIAO YUKUN</creator><creator>SHANGGUAN ZHAOHUI</creator><creator>YING JUNYU</creator><scope>EVB</scope></search><sort><creationdate>20200327</creationdate><title>sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition</title><author>LIU SHOUWEN ; CHEN SHASHA ; WEN BINGBING ; LIU YI ; LI FAN ; LIAO YUKUN ; SHANGGUAN ZHAOHUI ; YING JUNYU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN110929085A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LIU SHOUWEN</creatorcontrib><creatorcontrib>CHEN SHASHA</creatorcontrib><creatorcontrib>WEN BINGBING</creatorcontrib><creatorcontrib>LIU YI</creatorcontrib><creatorcontrib>LI FAN</creatorcontrib><creatorcontrib>LIAO YUKUN</creatorcontrib><creatorcontrib>SHANGGUAN ZHAOHUI</creatorcontrib><creatorcontrib>YING JUNYU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIU SHOUWEN</au><au>CHEN SHASHA</au><au>WEN BINGBING</au><au>LIU YI</au><au>LI FAN</au><au>LIAO YUKUN</au><au>SHANGGUAN ZHAOHUI</au><au>YING JUNYU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition</title><date>2020-03-27</date><risdate>2020</risdate><abstract>The invention provides a sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition. The scheme is realized from six aspectsof deep learning environment construction, generative adversarial network framework construction, questioning sample meta-semantic decomposition, response sample semantic cutting, response sample meta-semantic decomposition and response sample set expansion. Through the steps of establishing a deep learning training environment and a generative adversarial network framework, extracting meta-semantics of response samples of questioning samples and the like, enhancement of a generative model training sample set is finally realized.
本发明提供一种基于元语义分解的电力客服留言生成模型样本处理系统及方法。本发明从"深度学习环境搭建、生成对抗网络框架搭建、提问样本元语义分解、应答样本语义切割、应答样本元语义分解、应答样本集扩充"六个方面来实现此方案,通过搭建深度学习训练环境与生成对抗网络框架,接着通过对提问样本的应答样本的元语义提取等步骤,最终实现生成模型训练样本集的增强。</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN110929085A |
source | esp@cenet |
subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | sample processing system and method for electric power customer service message generation model based on meta-semantic decomposition |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T02%3A47%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LIU%20SHOUWEN&rft.date=2020-03-27&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN110929085A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |