{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:18Z","timestamp":1729225698999,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Named Entity Recognition is a crucial task in Natural Language Processing (NLP) which aims to identify the entities in text. Given an adequate amount of annotated data, Large Language Models (LLMs) have been shown to be effective in this task when fine-tuned. However, the performance of LLMs is severely affected when annotated datasets are limited. To alleviate this problem, adding synthetic data via Data Augmentation (DA) techniques is a viable approach. Even so, DA for token-level tasks suffers from two main limitations: (i) token-label misalignment problem; and (ii) quality of generated synthetic data. In this paper, we propose a novel prompt-based DA approach using contrastive learning. The proposed method can generate high-quality synthetic data while preserving the token-label correspondences. Experimental results demonstrate that the proposed approach, when compared against multiple baselines on well-known Named Entity Recognition (NER) datasets, achieves State-of-the-Art performance.<\/jats:p>","DOI":"10.3233\/faia240805","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:24:33Z","timestamp":1729171473000},"source":"Crossref","is-referenced-by-count":0,"title":["Prompt-Based Data Augmentation Using Contrastive Learning Under Scarcity of Annotated Data"],"prefix":"10.3233","author":[{"given":"Muhammad Uzair","family":"Ul Haq","sequence":"first","affiliation":[{"name":"Department of Mathematics \u201cTullio Levi-Civita\u201d, University of Padova, Via Trieste 63, 35121 Padova, Italy"},{"name":"Human Inspired Technology Research Centre, University of Padova, Via Luzzatti 4, 35122 Padova, Italy"},{"name":"Amajor SB S.p.A, Via Noventana 192, 35027 Noventa Padovana, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davide","family":"Rigoni","sequence":"additional","affiliation":[{"name":"Molecular Modelling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131, Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Sperduti","sequence":"additional","affiliation":[{"name":"Department of Mathematics \u201cTullio Levi-Civita\u201d, University of Padova, Via Trieste 63, 35121 Padova, Italy"},{"name":"Human Inspired Technology Research Centre, University of Padova, Via Luzzatti 4, 35122 Padova, Italy"},{"name":"Augmented Intelligence Center, Bruno Kessler Foundation, Via Sommarive 18, 38123 Povo, Italy"},{"name":"Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Povo, TN, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240805","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:24:33Z","timestamp":1729171473000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240805"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240805","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}