A large-scale dataset for korean document-level relation extraction from encyclopedia texts
Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the stu...
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| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2024-09, Vol.54 (17-18), p.8681-8701 |
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| container_title | Applied intelligence (Dordrecht, Netherlands) |
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| creator | Son, Suhyune Lim, Jungwoo Koo, Seonmin Kim, Jinsung Kim, Younghoon Lim, Youngsik Hyun, Dongseok Lim, Heuiseok |
| description | Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK (
T
oward Document-Level
R
elation
E
xtraction in
K
orean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language’s properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis. |
| doi_str_mv | 10.1007/s10489-024-05605-9 |
| format | Article |
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T
oward Document-Level
R
elation
E
xtraction in
K
orean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language’s properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis.</description><identifier>ISSN: 0924-669X</identifier><identifier>EISSN: 1573-7497</identifier><identifier>DOI: 10.1007/s10489-024-05605-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Annotations ; Artificial Intelligence ; Computer Science ; Cultural heritage ; Datasets ; Documents ; Encyclopedias ; Korean language ; Language ; Machines ; Manufacturing ; Mechanical Engineering ; Processes ; Qualitative analysis ; Statistical analysis ; Subject specialists</subject><ispartof>Applied intelligence (Dordrecht, Netherlands), 2024-09, Vol.54 (17-18), p.8681-8701</ispartof><rights>The Author(s) 2024</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c244t-87a83a982b9aef48d5183afa02ceecd9b6be162b05ef537209eb9f721889db683</cites><orcidid>0000-0002-9269-1157</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10489-024-05605-9$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10489-024-05605-9$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Son, Suhyune</creatorcontrib><creatorcontrib>Lim, Jungwoo</creatorcontrib><creatorcontrib>Koo, Seonmin</creatorcontrib><creatorcontrib>Kim, Jinsung</creatorcontrib><creatorcontrib>Kim, Younghoon</creatorcontrib><creatorcontrib>Lim, Youngsik</creatorcontrib><creatorcontrib>Hyun, Dongseok</creatorcontrib><creatorcontrib>Lim, Heuiseok</creatorcontrib><title>A large-scale dataset for korean document-level relation extraction from encyclopedia texts</title><title>Applied intelligence (Dordrecht, Netherlands)</title><addtitle>Appl Intell</addtitle><description>Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK (
T
oward Document-Level
R
elation
E
xtraction in
K
orean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language’s properties. 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Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK (
T
oward Document-Level
R
elation
E
xtraction in
K
orean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language’s properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10489-024-05605-9</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-9269-1157</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Applied intelligence (Dordrecht, Netherlands), 2024-09, Vol.54 (17-18), p.8681-8701 |
| issn | 0924-669X 1573-7497 |
| language | eng |
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| source | SpringerLink Journals |
| subjects | Annotations Artificial Intelligence Computer Science Cultural heritage Datasets Documents Encyclopedias Korean language Language Machines Manufacturing Mechanical Engineering Processes Qualitative analysis Statistical analysis Subject specialists |
| title | A large-scale dataset for korean document-level relation extraction from encyclopedia texts |
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