We present an approach to topical classification of biographical text fragments that takes into account the nearest context of classified fragments using a neural network with several inputs. The choice of the model architecture is based on the assumption that since texts written in a natural language differ in consistency and coherence, the context of a passage can be used as additional input data. The model was trained and tested on the biographical corpus compiled by ourselves. The results obtained using the proposed approach outperformed the results of models that do not take into account the context of the passage.

中文翻译：

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考虑到最近上下文的文本片段的主题分类

我们提出了一种对传记文字片段进行主题分类的方法，该方法考虑了使用带有多个输入的神经网络对分类片段的最近上下文。模型体系结构的选择基于以下假设：由于以自然语言编写的文本的一致性和连贯性不同，因此段落的上下文可以用作其他输入数据。该模型在我们自己编写的传记语料库上进行了培训和测试。使用建议的方法获得的结果优于未考虑段落上下文的模型的结果。