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Process."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    Metaphor, as a common type of linguistic expression, helps people intuitively understand complex concepts in communication, writing, and cognition. Metaphor components, including source-domain words and target-domain words, are critical elements for metaphor identification and interpretation. This article focuses on metaphor components and proposes a metaphor components identification framework employing\n                    <jats:bold>F<\/jats:bold>\n                    eedback-enhanced\n                    <jats:bold>F<\/jats:bold>\n                    eature-driven\n                    <jats:bold>I<\/jats:bold>\n                    n-\n                    <jats:bold>C<\/jats:bold>\n                    ontext\n                    <jats:bold>L<\/jats:bold>\n                    earning (FF-ICL) based on the large language model (LLM). Specifically, in-context learning and feedback mechanisms inspired by human learning are integrated. Firstly, a machine feedback mechanism is designed to perform prior predictions on training samples, constructing a candidate demonstration pool enriched with prediction results and feedback information. Secondly, a multi-head graph attention network (GAT) is introduced to capture the linguistic and structural information embedded in metaphorical expressions, producing feature-rich representations and establishing a vector repository. Based on the repository, the framework retrieves demonstrations most relevant to the input query across different feature dimensions, incorporating in-context prompts to effectively fine-tune the LLM. Experiments and analyses on public datasets demonstrate the superiority of FF-ICL. Furthermore, the metaphor concept mapping experiment validates the crucial role of metaphor components in downstream computational metaphor tasks. Relevant data and codes are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/WXLJZ\/FF-ICL\">https:\/\/github.com\/WXLJZ\/FF-ICL<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3801739","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:56:39Z","timestamp":1773348999000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Metaphor Components Identification with Feedback-enhanced Feature-driven In-context Learning"],"prefix":"10.1145","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3267-5002","authenticated-orcid":false,"given":"Hongde","family":"Liu","sequence":"first","affiliation":[{"name":"Zhengzhou University","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9169-9553","authenticated-orcid":false,"given":"Chenyuan","family":"He","sequence":"additional","affiliation":[{"name":"Zhengzhou University","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4355-6415","authenticated-orcid":false,"given":"Senbin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhengzhou University","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1970-3437","authenticated-orcid":false,"given":"Changyong","family":"Niu","sequence":"additional","affiliation":[{"name":"Zhengzhou University","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0481-0740","authenticated-orcid":false,"given":"Yuxiang","family":"Jia","sequence":"additional","affiliation":[{"name":"Zhengzhou University","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4324\/9781315782119-3","volume-title":"Proceedings of the Theories of Memory","author":"Barsalou Lawrence W.","year":"2019","unstructured":"Lawrence W. 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