{"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/neural-fine-grained-entity-type","title":"Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss","arxiv_id":"1803.03378","date":"2018-03-09","proceeding":"NAACL 2018 6","authors":["Peng Xu","Denilson Barbosa"],"abstract":"The task of Fine-grained Entity Type Classification (FETC) consists of\nassigning types from a hierarchy to entity mentions in text. Existing methods\nrely on distant supervision and are thus susceptible to noisy labels that can\nbe out-of-context or overly-specific for the training sentence. Previous\nmethods that attempt to address these issues do so with heuristics or with the\nhelp of hand-crafted features. Instead, we propose an end-to-end solution with\na neural network model that uses a variant of cross- entropy loss function to\nhandle out-of-context labels, and hierarchical loss normalization to cope with\noverly-specific ones. Also, previous work solve FETC a multi-label\nclassification followed by ad-hoc post-processing. In contrast, our solution is\nmore elegant: we use public word embeddings to train a single-label that\njointly learns representations for entity mentions and their context. We show\nexperimentally that our approach is robust against noise and consistently\noutperforms the state-of-the-art on established benchmarks for the task.","url_abs":"http://arxiv.org/abs/1803.03378v2","url_pdf":"http://arxiv.org/pdf/1803.03378v2.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":"neural-fine-grained-entity-type","repo_url":"https://github.com/billy-inn/NFETC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"neural-fine-grained-entity-type","repo_url":"https://github.com/YoungXiyuan/DCA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neural-fine-grained-entity-type","repo_url":"https://github.com/ZacharyChenpk/Local_ETHZ","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.03378","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}