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Targeted Aspect-based Financial Sentiment Analysis (TABFSA) is an example of such complicated tasks, as it involves processes like information extraction, information specification, and domain adaptation. However, little is known about the design principles of such hybrid models leveraging external lexical knowledge. To fill this gap, we\n            <jats:bold>define<\/jats:bold>\n            anterior, parallel, and posterior knowledge integration and\n            <jats:bold>propose<\/jats:bold>\n            incorporating multiple lexical knowledge sources strategically into the fine-tuning process of pre-trained transformer models for TABFSA. Experiments on the Financial Opinion mining and Question Answering challenge (FiQA) Task 1 and SemEval 2017 Task 5 datasets show that the knowledge-enabled models systematically improve upon their plain deep learning counterparts, and some outperform\n            <jats:bold>state-of-the-art<\/jats:bold>\n            results reported in terms of aspect sentiment analysis error. We discover that parallel knowledge integration is the most effective and domain-specific lexical knowledge is more important according to our ablation analysis.\n          <\/jats:p>","DOI":"10.1145\/3580480","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T11:52:22Z","timestamp":1675079542000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7856-3140","authenticated-orcid":false,"given":"Kelvin","family":"Du","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5751-3937","authenticated-orcid":false,"given":"Frank","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Analytics, National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-1280","authenticated-orcid":false,"given":"Erik","family":"Cambria","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2023,6,23]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"540","volume-title":"EMNLP","author":"Akhtar Md Shad","year":"2017","unstructured":"Md Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, and Pushpak Bhattacharyya. 2017. 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