Emotional target extraction model based on multi-word embedding fusion and attention mechanism
The invention relates to an emotion target extraction model ME-ATT-CRF based on multi-word embedding fusion and an attention mechanism. According to the model, three types of word embedding are adopted for fusion, universal embedding and specific domain embedding are adopted, the condition that word...
Saved in:
Main Authors | , |
---|---|
Format | Patent |
Language | Chinese English |
Published |
31.05.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The invention relates to an emotion target extraction model ME-ATT-CRF based on multi-word embedding fusion and an attention mechanism. According to the model, three types of word embedding are adopted for fusion, universal embedding and specific domain embedding are adopted, the condition that word forms can reflect part-of-speech to a certain extent and then affect a labeling result is considered, morphological information of character-level convolutional learning words is added to enrich feature representation, and character-level features are extracted. Under the condition that no extra supervision is used, the model achieves a better effect. In addition, a self-attention mechanism is introduced into a hidden layer of the model, so that the model can automatically learn association and weights among different words in an input text, context semantics are fully understood, and more attention is paid to target words to be extracted. Experimental verification and comparison are carried out on the four data s |
---|---|
AbstractList | The invention relates to an emotion target extraction model ME-ATT-CRF based on multi-word embedding fusion and an attention mechanism. According to the model, three types of word embedding are adopted for fusion, universal embedding and specific domain embedding are adopted, the condition that word forms can reflect part-of-speech to a certain extent and then affect a labeling result is considered, morphological information of character-level convolutional learning words is added to enrich feature representation, and character-level features are extracted. Under the condition that no extra supervision is used, the model achieves a better effect. In addition, a self-attention mechanism is introduced into a hidden layer of the model, so that the model can automatically learn association and weights among different words in an input text, context semantics are fully understood, and more attention is paid to target words to be extracted. Experimental verification and comparison are carried out on the four data s |
Author | KUANG LIJUAN DAI XIANHUA |
Author_xml | – fullname: DAI XIANHUA – fullname: KUANG LIJUAN |
BookMark | eNqNi7sOgkAQRSm08PUP4wdQENDE0hCMlZW1ZGAvuMnuLGGH6Ocboh9gdXNOzl0nCwmCVfKofFAbhB0pjz2U8NaR29mRDwaOGo4wNOPk1KavMBqCb2CMlZ66Kc4piyFWhXyPaJ8sNvptsuzYRex-u0n2l-peXlMMoUYcuIVA6_KWZcXhWJwO-Tn_p_kAwU4-0Q |
ContentType | Patent |
DBID | EVB |
DatabaseName | esp@cenet |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: EVB name: esp@cenet url: http://worldwide.espacenet.com/singleLineSearch?locale=en_EP sourceTypes: Open Access Repository |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Chemistry Sciences Physics |
DocumentTitleAlternate | 一种基于多种词嵌入融合与注意力机制的情感目标抽取模型 |
ExternalDocumentID | CN114564953A |
GroupedDBID | EVB |
ID | FETCH-epo_espacenet_CN114564953A3 |
IEDL.DBID | EVB |
IngestDate | Fri Jul 19 12:58:14 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | Chinese English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-epo_espacenet_CN114564953A3 |
Notes | Application Number: CN202210185044 |
OpenAccessLink | https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220531&DB=EPODOC&CC=CN&NR=114564953A |
ParticipantIDs | epo_espacenet_CN114564953A |
PublicationCentury | 2000 |
PublicationDate | 20220531 |
PublicationDateYYYYMMDD | 2022-05-31 |
PublicationDate_xml | – month: 05 year: 2022 text: 20220531 day: 31 |
PublicationDecade | 2020 |
PublicationYear | 2022 |
RelatedCompanies | MEDIUM-MOUNTAIN UNIVERSITY |
RelatedCompanies_xml | – name: MEDIUM-MOUNTAIN UNIVERSITY |
Score | 3.530922 |
Snippet | The invention relates to an emotion target extraction model ME-ATT-CRF based on multi-word embedding fusion and an attention mechanism. According to the model,... |
SourceID | epo |
SourceType | Open Access Repository |
SubjectTerms | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
Title | Emotional target extraction model based on multi-word embedding fusion and attention mechanism |
URI | https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220531&DB=EPODOC&locale=&CC=CN&NR=114564953A |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NT8JAEJ0gft4UNYofWRPDrRFLW-DQGNmWEBMKMWg4SbbbbcBIIbTExF_vzBbEix67TTfdSWbe7O57MwC3TrMW2xIzN8u8F7hBcaQR1lXVEIgnIm46UjVJ4NwNnM6L9TS0hwV4X2thdJ3QT10cET1Kor9nOl7PN4dYnuZWpnfhBIdmD-2B61VWu2NSjWJQ8Vqu3-95PV7h3OVBJXh2Me23HeJSPm7BNqbRdfIG_7VFqpT5b0hpH8JOH2dLsiMofI1LsM_XnddKsNddXXiXYFczNGWKgysvTI_hzc9774gPlhO5GUbYRa5QYLq1DSNwihg9EmHQwPVGTE1DFRFUsXhJZ2RMJBGj8ppJ_qEiEfAknZ7ATdsf8I6Bvzz6sc-IB5vV1U6hmMwSdQbMMgUBsFJ2XLdk1BDKEiq0pGnKqqrKxjmU_56n_N_LCzggW-eX6JdQzBZLdYXYnIXX2qjfrPiVWw |
link.rule.ids | 230,309,783,888,25576,76876 |
linkProvider | European Patent Office |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NT8JAEJ0gfuBNUaP4tSaGWyOWtsChMdKWoEIhBg0nyXa7jRgphJaY-Oud2YJ60WO36aY7ycyb3X1vBuDSalQjU2DmZujXHDcoltCCmqxoHPGERw1LyAYJnLu-1X4y7ofmMAdvKy2MqhP6oYojokcJ9PdUxevZzyGWq7iVyVUwxqHpTWtgu-Xl7phUoxhU3Kbt9Xtuzyk7ju34Zf_RxrTftIhLebsG65hi18gbvOcmqVJmvyGltQMbfZwtTnch9_lahIKz6rxWhK3u8sK7CJuKoSkSHFx6YbIHL17We4e_s4zIzTDCzjOFAlOtbRiBU8jokQiDGq43ZHISyJCgikULOiNjPA4ZldeMsw8liYDHyWQfLlrewGlr-Mujb_uMHP9nddUDyMfTWB4CM3ROACylGdUMEda5NLgMDKHroiIron4Epb_nKf338hwK7UG3M-rc-Q_HsE12zy7UTyCfzhfyFHE6Dc6Ugb8A2zyYTg |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Apatent&rft.title=Emotional+target+extraction+model+based+on+multi-word+embedding+fusion+and+attention+mechanism&rft.inventor=DAI+XIANHUA&rft.inventor=KUANG+LIJUAN&rft.date=2022-05-31&rft.externalDBID=A&rft.externalDocID=CN114564953A |