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Although the problem has been widely explored, no previous studies have attempted to identify Chinese classical poetry. Inspired by Trudgill\u2019s seminal work on stylistic variation as a function of occupation, we present a novel Domain-Knowledge Transformer model to predict a poet\u2019s occupation through their poems\u2019 writing styles. Different from other Indo-European languages, Chinese has rarely used characters and two types of writing forms: traditional Chinese and simplified Chinese. To tackle these problems, we use the language-related component to standardize our input. We also use alphabetization to satisfy the restrictions on rhyming rules and tonal styles. As a special literal form, traditional domain knowledge, for example, named entities, themes, ages and the official career path, is valuable for poet occupation profiling. However, due to the lack of appropriate annotation datasets, it is difficult to recognize these features. Therefore, we proposed the domain knowledge component employing the latent Dirichletal location model to capture the extra theme information and establish named entity dictionaries to recognize the named entity of the datasets in this study. Finally, in the deep learning component, we combine Transformer with a convolutional neural network (CNN) model to perform occupation profiling. The experimental results suggest that our model is effective in this task. Moreover, the results demonstrate an account of other social attribution features of poetry style that are predictive of occupation in this domain.<\/jats:p>","DOI":"10.1007\/s44196-023-00386-4","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T10:02:20Z","timestamp":1703152940000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Domain Knowledge Transformer Model for Occupation Profiling"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5432-0881","authenticated-orcid":false,"given":"Zhou","family":"Ai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Yijia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Mingyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"386_CR1","unstructured":"Chambers, J. 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I would like to declare on behalf of my coauthors that the manuscript has been submitted solely to this journal, has not been published previously and is not under consideration or in press for publication elsewhere, in whole or in part.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"The authors have all read and accepted the manuscript and approve its submission. There are no other persons who satisfy the criteria for authorship.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"198"}}