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Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We
doi:10.18653/v1/e17-1075
dblp:conf/eacl/AbhishekAA17
fatcat:bgoj7mbfvfexxdbgbtp47q5fgm