{"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/eelection-at-semeval-2017-task-10-ensemble-of","title":"EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION","arxiv_id":"1704.02215","date":"2017-04-07","proceeding":"SEMEVAL 2017 8","authors":["Steffen Eger","Erik-Lân Do Dinh","Ilia Kuznetsov","Masoud Kiaeeha","Iryna Gurevych"],"abstract":"This paper describes our approach to the SemEval 2017 Task 10: \"Extracting\nKeyphrases and Relations from Scientific Publications\", specifically to Subtask\n(B): \"Classification of identified keyphrases\". We explored three different\ndeep learning approaches: a character-level convolutional neural network (CNN),\na stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM.\nFrom these approaches, we created an ensemble of differently\nhyper-parameterized systems, achieving a micro-F1-score of 0.63 on the test\ndata. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four\naccording to this official score. However, we erroneously trained 2 out of 3\nneural nets (the stacker and the CNN) on only roughly 15% of the full data,\nnamely, the original development set. When trained on the full data\n(training+development), our ensemble has a micro-F1-score of 0.69. Our code is\navailable from https://github.com/UKPLab/semeval2017-scienceie.","url_abs":"http://arxiv.org/abs/1704.02215v2","url_pdf":"http://arxiv.org/pdf/1704.02215v2.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":"eelection-at-semeval-2017-task-10-ensemble-of","repo_url":"https://github.com/UKPLab/semeval2017-scienceie","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}