{"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/fine-grained-entity-type-classification-by","title":"Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings","arxiv_id":"1702.06709","date":"2017-02-22","proceeding":"EACL 2017 4","authors":["abhishek","Ashish Anand","Amit Awekar"],"abstract":"Fine-grained entity type classification (FETC) is the task of classifying an\nentity mention to a broad set of types. Distant supervision paradigm is\nextensively used to generate training data for this task. However, generated\ntraining data assigns same set of labels to every mention of an entity without\nconsidering its local context. Existing FETC systems have two major drawbacks:\nassuming training data to be noise free and use of hand crafted features. Our\nwork overcomes both drawbacks. We propose a neural network model that jointly\nlearns entity mentions and their context representation to eliminate use of\nhand crafted features. Our model treats training data as noisy and uses\nnon-parametric variant of hinge loss function. Experiments show that the\nproposed model outperforms previous state-of-the-art methods on two publicly\navailable datasets, namely FIGER (GOLD) and BBN with an average relative\nimprovement of 2.69% in micro-F1 score. Knowledge learnt by our model on one\ndataset can be transferred to other datasets while using same model or other\nFETC systems. These approaches of transferring knowledge further improve the\nperformance of respective models.","url_abs":"http://arxiv.org/abs/1702.06709v1","url_pdf":"http://arxiv.org/pdf/1702.06709v1.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":"fine-grained-entity-type-classification-by","repo_url":"https://github.com/abhipec/fnet","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":"https://app.syntology.ai/?focus=1702.06709","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}