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Literary Genre Recognition among Polish Blog Posts

机译:波兰博客帖子中的文学类型认可

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Robust methods have been proposed for content and topic-based text classification, as well authorship attribution in stylometry. However, the problem of a fine-grained literary genre (style) recognition is much less studied. We present several approaches to the recognition of eight literary genres manually annotated in a large corpus of Polish blogs. Different text representations were combined with neural network classifiers, including deep, recursive neural networks. Very good results were achieved for the representation of blog posts with the help of pre-trained fastText word embeddings and the Bi-GRU recursive deep neural network as a classifier. As the observed good performance of this classifier could be a result of topical bias across genres, experiments on a selected sub-corpus with a reduced dominance of the most frequent topic were also conducted with no significant change observed.
机译:已经提出了基于内容和主题的文本分类的强大方法,以及演奏时间的作者归属。 然而,精细粒度的文学类型(风格)识别的问题较小。 我们提出了识别八种文学类型的若干方法,手动注释在大型波兰博客的大语料库中。 不同的文本表示与神经网络分类器相结合,包括深度,递归神经网络。 博客帖子的代表借助预先接受过的FastText Word Embeddings和Bi-Gru递归深神经网络作为分类器,实现了非常好的结果。 由于观察到该分类器的良好性能可能是局部局部突破的结果,还进行了在最常见的话题的降低主导地位的所选子语料库的实验,没有观察到显着变化。

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