{"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/expr-at-semeval-2018-task-9-a-combined","title":"EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery","arxiv_id":null,"date":"2018-06-01","proceeding":"SEMEVAL 2018 6","authors":["Ahmad Issa Alaa Aldine","Mounira Harzallah","Giuseppe Berio","Nicolas B{\\'e}chet","Ahmad Faour"],"abstract":"In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.","url_abs":"https://aclanthology.org/S18-1150","url_pdf":"https://aclanthology.org/S18-1150.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":[],"tasks":[{"task_slug":"hypernym-discovery","task_name":"Hypernym Discovery"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hypernym-discovery-on-medical-domain","task":"Hypernym Discovery","dataset":"Medical domain","model":"EXPR","rank_in_archive_order":5,"of":8,"metrics":{"MAP":"13.77","MRR":"40.76","P@5":"12.76"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}