Fine grained entity type classification using type semantic representation for noisy label reduction
The training data of fine-grained entity typing(FET)is usually generated by the distant supervision based on knowledge base,this process inevitably introduces noise type labels.The existing work mostly models the probabili-ty distribution of the training data and annotation types,and lacks the semantic learning of fine-grained types,cau-sing the problem of the usage of types unrelated to the entity context during models learning.This paper proposes a fine-grained entity classification method for label noise reduction based on the semantic representation of fine-grained types.First,it learns the representation of some fine-grained types from the data with a unique fine-grained type path in the training set,and learns the representation of the rest fine-grained types by the combination of the relationship information between fine-grained types.Second,select the fine-grained entity type in the training data annotation fine-grained type set that is most similar to the semantic information of the entity context as target types,then,select Top-K similar sentences from the dataset to aggregate fine-grained semantic information.Last,it learns final fine-grained entity classification model based on the aggregated information.Experimental results and analysis on datasets demonstrate that our model effectively selects the fine-grained type that best matches the semantic infor-mation of the entity context from the fine-grained types set annotated in the training data,and is able to achieve the effect of improving the accuracy of fine-grained entity.
万方数据
维普
