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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Extraction of Application Programming Interfaces (APIs) and their semantic relations from unstructured text (e.g., Stack Overflow) is a fundamental work for software engineering tasks (e.g., API recommendation). However, existing approaches are rule based and sequence labeling based. They must manually enumerate the rules or label data for a wide range of sentence patterns, which involves a significant amount of labor overhead and is exacerbated by morphological and common-word ambiguity. In contrast to matching or labeling API entities and relations, this article formulates heterogeneous API extraction and API relation extraction task as a sequence-to-sequence generation task and proposes the API Entity-Relation Joint Extraction framework (AERJE), an API entity-relation joint extraction model based on the large pre-trained language model. After training on a small number of ambiguous but correctly labeled data, AERJE builds a multi-task architecture that extracts API entities and relations from unstructured text using dynamic prompts. We systematically evaluate AERJE on a set of long and ambiguous sentences from Stack Overflow. The experimental results show that AERJE achieves high accuracy and discrimination ability in API entity-relation joint extraction, even with zero or few-shot fine-tuning.<\/jats:p>","DOI":"10.1145\/3607188","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T12:13:28Z","timestamp":1688386408000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8877-4267","authenticated-orcid":false,"given":"Qing","family":"Huang","sequence":"first","affiliation":[{"name":"Jiangxi Normal University, School of Computer Information Engineering, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6047-1554","authenticated-orcid":false,"given":"Yanbang","family":"Sun","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, School of Computer Information Engineering, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7663-1421","authenticated-orcid":false,"given":"Zhenchang","family":"Xing","sequence":"additional","affiliation":[{"name":"CSIRO\u2019s Data61 &amp; Australian National University, College of Engineering and Computer Science, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9299-4804","authenticated-orcid":false,"given":"Min","family":"Yu","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, School of Computer Information Engineering, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2540-973X","authenticated-orcid":false,"given":"Xiwei","family":"Xu","sequence":"additional","affiliation":[{"name":"CSIRO\u2019s Data61, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9466-1672","authenticated-orcid":false,"given":"Qinghua","family":"Lu","sequence":"additional","affiliation":[{"name":"CSIRO\u2019s Data61, Australia"}]}],"member":"320","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Qing Huang Zhiqiang Yuan Zhenchang Xing Zhengkang Zuo Changjing Wang and Xin Xia. 2023. 1+1>2: Programming know-what and know-how knowledge fusion semantic enrichment and coherent application. 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