{"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/italian-event-detection-goes-deep-learning","title":"Italian Event Detection Goes Deep Learning","arxiv_id":"1810.02229","date":"2018-10-04","proceeding":null,"authors":["Tommaso Caselli"],"abstract":"This paper reports on a set of experiments with different word embeddings to\ninitialize a state-of-the-art Bi-LSTM-CRF network for event detection and\nclassification in Italian, following the EVENTI evaluation exercise. The net-\nwork obtains a new state-of-the-art result by improving the F1 score for\ndetection of 1.3 points, and of 6.5 points for classification, by using a\nsingle step approach. The results also provide further evidence that embeddings\nhave a major impact on the performance of such architectures.","url_abs":"http://arxiv.org/abs/1810.02229v1","url_pdf":"http://arxiv.org/pdf/1810.02229v1.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":"italian-event-detection-goes-deep-learning","repo_url":"https://github.com/tommasoc80/Event_detection_CLiC-it2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}