Relationship extraction method based on entity mapping
The invention belongs to the field of text information processing, and discloses a relation extraction method based on entity mapping, which comprises the following steps of: firstly, acquiring original data, marking the data and dividing the data into a training set and a test set; then, constructi...
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| creator | WANG SHUAI TANG WENZHONG TANG HONGMEI ZHU DIXIONGXIAO |
| description | The invention belongs to the field of text information processing, and discloses a relation extraction method based on entity mapping, which comprises the following steps of: firstly, acquiring original data, marking the data and dividing the data into a training set and a test set; then, constructing a CasRelBLCF network model, including sentence semantic embedding, sentence head entity decoding and sentence tail entity decoding; then, the CasRelBLCF network model is trained, and a relation extraction model is obtained; and finally, inputting sentences into the relation extraction model, and extracting a relation triple. According to the relation extraction method based on entity mapping, the probability of illegal label generation can be reduced, the problem of sample imbalance in a tail entity decoder is effectively solved, and the extraction accuracy and the recall rate are effectively improved.
本发明属于文本信息处理领域，公开了一种基于实体映射的关系抽取方法，包括以下步骤：首先，获取原始数据，对数据进行标注后划分为训练集和测试集；然后，构建CasRelBLCF网络模型，包括句子语义嵌入、句子头实体解码、句子尾实体 |
| format | Patent |
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本发明属于文本信息处理领域，公开了一种基于实体映射的关系抽取方法，包括以下步骤：首先，获取原始数据，对数据进行标注后划分为训练集和测试集；然后，构建CasRelBLCF网络模型，包括句子语义嵌入、句子头实体解码、句子尾实体</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2023</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=20231208&DB=EPODOC&CC=CN&NR=117196030A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231208&DB=EPODOC&CC=CN&NR=117196030A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG SHUAI</creatorcontrib><creatorcontrib>TANG WENZHONG</creatorcontrib><creatorcontrib>TANG HONGMEI</creatorcontrib><creatorcontrib>ZHU DIXIONGXIAO</creatorcontrib><title>Relationship extraction method based on entity mapping</title><description>The invention belongs to the field of text information processing, and discloses a relation extraction method based on entity mapping, which comprises the following steps of: firstly, acquiring original data, marking the data and dividing the data into a training set and a test set; then, constructing a CasRelBLCF network model, including sentence semantic embedding, sentence head entity decoding and sentence tail entity decoding; then, the CasRelBLCF network model is trained, and a relation extraction model is obtained; and finally, inputting sentences into the relation extraction model, and extracting a relation triple. According to the relation extraction method based on entity mapping, the probability of illegal label generation can be reduced, the problem of sample imbalance in a tail entity decoder is effectively solved, and the extraction accuracy and the recall rate are effectively improved.
本发明属于文本信息处理领域，公开了一种基于实体映射的关系抽取方法，包括以下步骤：首先，获取原始数据，对数据进行标注后划分为训练集和测试集；然后，构建CasRelBLCF网络模型，包括句子语义嵌入、句子头实体解码、句子尾实体</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDALSs1JLMnMzyvOyCxQSK0oKUpMBnEVclNLMvJTFJISi1NTFID81LySzJJKhdzEgoLMvHQeBta0xJziVF4ozc2g6OYa4uyhm1qQH59aXJCYnJqXWhLv7GdoaG5oaWZgbOBoTIwaANOZLe4</recordid><startdate>20231208</startdate><enddate>20231208</enddate><creator>WANG SHUAI</creator><creator>TANG WENZHONG</creator><creator>TANG HONGMEI</creator><creator>ZHU DIXIONGXIAO</creator><scope>EVB</scope></search><sort><creationdate>20231208</creationdate><title>Relationship extraction method based on entity mapping</title><author>WANG SHUAI ; TANG WENZHONG ; TANG HONGMEI ; ZHU DIXIONGXIAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117196030A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>WANG SHUAI</creatorcontrib><creatorcontrib>TANG WENZHONG</creatorcontrib><creatorcontrib>TANG HONGMEI</creatorcontrib><creatorcontrib>ZHU DIXIONGXIAO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WANG SHUAI</au><au>TANG WENZHONG</au><au>TANG HONGMEI</au><au>ZHU DIXIONGXIAO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Relationship extraction method based on entity mapping</title><date>2023-12-08</date><risdate>2023</risdate><abstract>The invention belongs to the field of text information processing, and discloses a relation extraction method based on entity mapping, which comprises the following steps of: firstly, acquiring original data, marking the data and dividing the data into a training set and a test set; then, constructing a CasRelBLCF network model, including sentence semantic embedding, sentence head entity decoding and sentence tail entity decoding; then, the CasRelBLCF network model is trained, and a relation extraction model is obtained; and finally, inputting sentences into the relation extraction model, and extracting a relation triple. According to the relation extraction method based on entity mapping, the probability of illegal label generation can be reduced, the problem of sample imbalance in a tail entity decoder is effectively solved, and the extraction accuracy and the recall rate are effectively improved.
本发明属于文本信息处理领域，公开了一种基于实体映射的关系抽取方法，包括以下步骤：首先，获取原始数据，对数据进行标注后划分为训练集和测试集；然后，构建CasRelBLCF网络模型，包括句子语义嵌入、句子头实体解码、句子尾实体</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Relationship extraction method based on entity mapping |
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