Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. Hover, the type labels so obtained from knowledge bases are often noisy ( i.e. , incorrect for the entity mention's local context).  define a  new  task,  Label Noise Reduction in Entity Typing  (LNR), to be the automatic identification of correct type labels (type-paths) for  training examples , given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity beten entity types pose unique challenges for solving the LNR task.  propose a general framework, called  PLE , to  jointly  embed entity mentions, text features and entity types into the same low-dimensional space where, in that space,  objects whose types are semantically close have similar representations . Then  estimate the type-path for  each  training example in a top-down manner using the learned embeddings.  formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to  noisy labels  and faithfully models  type correlation  derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.
['Xiang Ren', 'Wenqi He', 'Meng Qu', 'Clare R. Voss', 'Heng Ji', 'Jiawei Han']
I am writing a research paper on a idea Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
[{"from": "human", "value": "I am writing a research paper on a idea Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. Hover, the type labels so obtained from knowledge bases are often noisy ( i.e. , incorrect for the entity mention's local context).  define a  new  task,  Label Noise Reduction in Entity Typing  (LNR), to be the automatic identification of correct type labels (type-paths) for  training examples , given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity beten entity types pose unique challenges for solving the LNR task.  propose a general framework, called  PLE , to  jointly  embed entity mentions, text features and entity types into the same low-dimensional space where, in that space,  objects whose types are semantically close have similar representations . Then  estimate the type-path for  each  training example in a top-down manner using the learned embeddings.  formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to  noisy labels  and faithfully models  type correlation  derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method."}]