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A Fine-Grained Entity Typing Method Combined with Features

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Abstract

Using the fine-grained entity typing method of distant supervision, when assigning type labels to entity mention, since the knowledge base contains all type labels of the entity, noisy labels will be introduced. This paper proposed a Fine-grained Entity Typing model combined with Features (FETF) to reduce the negative impact of noisy labels. It is different from the previous methods such as manual annotation and heuristic rule pruning. The model not only improves the classification efficiency, but also does not need to reduce the size of the training set, which can improve the overall performance of the classification model. FETF divides the training set into clean dataset and noisy dataset according to the type numbers of entity mention in candidate type set, and constructs different objective functions for them to achieve the purpose of reducing the impact of noisy labels. At the same time, FETF can use the feature generator to jointly learn the relational features of entity mention - type label, as well as the similarity and hierarchical features of type label - type label. The feature generator extracts semantic features other than context, so as to help the fine-grained entity typing model to assign type labels to entity mention. In addition, we introduce adversarial training in the context processor, which can effectively alleviate the model overfitting noisy labels, and improve the robustness and generalization ability of the model. Experimental results on the public datasets show that the method proposed in this paper can effectively alleviate the negative impact of noisy labels on the fine-grained entity typing model, and outperforms previous methods in accuracy, Macro F1 value and Micro F1 value.

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Notes

  1. The baselines results are reported on [36].

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  1. School of Computer and Information, Anhui Polytechnic University, 241000, Wuhu, China

    Zhou Qi, Tao Wan & Cheng Fei

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  1. Zhou Qi
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  2. Tao Wan
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  3. Cheng Fei
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Correspondence to Tao Wan.

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Qi, Z., Wan, T. & Fei, C. A Fine-Grained Entity Typing Method Combined with Features. Neural Process Lett 54, 3793–3809 (2022). https://doi.org/10.1007/s11063-022-10786-w

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