结合双向长短期记忆与子词嵌入的作者归属
Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution
AI总结：
提出基于BLSTM与二维CNN的方法，利用子词信息捕捉序列写作风格，解决作者归属中未登录词问题，在CCAT50和Twitter上准确率分别提升1.07%和0.96%。
摘要：
从多个候选作者中揭示给定文本文档作者的问题被称为作者归属。多种基于词的文体标记已成功应用于深度学习方法中以处理作者归属的内在问题。然而，基于词的作者归属系统的性能受限于训练语料库的词汇量。文献建议使用基于字符的文体标记作为替代方案，以克服未登录词问题。但是，基于字符的方法往往无法捕捉文本中词的序列关系，这成为进一步提升的鸿沟。本文探讨的问题是，是否有可能在保留词的序列上下文的同时，解决文本文档中未登录词的歧义问题。因此，提出了一种基于双向长短期记忆（BLSTM）与二维卷积神经网络（CNN）的方法，以捕捉用于作者归属的序列写作风格。BLSTM被用于利用子词信息获取特征之间的序列关系。二维CNN被应用于理解来自无标签输入文本的风格的局部句法位置。所提出的方法在CCAT50、IMDb62、Blog50和Twitter50公共语料库上与众多最先进方法进行了实验评估。实验结果表明，在CCAT50和Twitter上准确率分别提升了1.07%和0.96%，并在其余数据集上产生了可比的结果。
英文摘要：
The problem of unveiling the author of a given text document from multiple candidate authors is called authorship attribution. Manifold word-based stylistic markers have been successfully used in deep learning methods to deal with the intrinsic problem of authorship attribution. Unfortunately, the performance of word-based authorship attribution systems is limited by the vocabulary of the training corpus. Literature has recommended character-based stylistic markers as an alternative to overcome the hidden word problem. However, character-based methods often fail to capture the sequential relationship of words in texts which is a chasm for further improvement. The question addressed in this paper is whether it is possible to address the ambiguity of hidden words in text documents while preserving the sequential context of words. Consequently, a method based on bidirectional long short-term memory (BLSTM) with a 2-dimensional convolutional neural network (CNN) is proposed to capture sequential writing styles for authorship attribution. The BLSTM was used to obtain the sequential relationship among characteristics using subword information. The 2-dimensional CNN was applied to understand the local syntactical position of the style from unlabeled input text. The proposed method was experimentally evaluated against numerous state-of-the-art methods across the public corporal of CCAT50, IMDb62, Blog50, and Twitter50. Experimental results indicate accuracy improvement of 1.07\%, and 0.96\% on CCAT50 and Twitter, respectively, and produce comparable results on the remaining datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
