{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/andi-at-semeval-2021-task-1-predicting","title":"ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources","arxiv_id":null,"date":"2021-08-01","proceeding":"SEMEVAL 2021","authors":["Armand Rotaru"],"abstract":"In this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context. Our approach relies on a combination of distributional models, both context-dependent and context-independent, together with behavioural norms and lexical resources.","url_abs":"https://aclanthology.org/2021.semeval-1.84","url_pdf":"https://aclanthology.org/2021.semeval-1.84.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"andi-at-semeval-2021-task-1-predicting","repo_url":"https://github.com/armandrotaru/teamandi-lcp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"lexical-complexity-prediction","task_name":"Lexical Complexity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}