论文标题
一个统治所有这些的图：使用NLP和图形神经网络分析托尔金的传奇
One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium
论文作者
论文摘要
自然语言处理和机器学习具有相当高的计算文学研究。同样，文学角色的共同发生网络的构建，以及它们使用社交网络分析和网络科学方法的分析，为文学文本的微观和宏观级结构提供了见解。结合了这些观点，在这项工作中，我们研究了从J.R.R.的文本语料库中提取的角色网络托尔金的传奇人物。我们表明，这种观点有助于我们分析和可视化托尔金作品的叙事风格。在解决角色分类，嵌入和共发生预测时，我们进一步研究了最新的图形神经网络的优势，而不是流行的单词嵌入方法。我们的结果突出了计算文学研究中图形学习的巨大潜力。
Natural Language Processing and Machine Learning have considerably advanced Computational Literary Studies. Similarly, the construction of co-occurrence networks of literary characters, and their analysis using methods from social network analysis and network science, have provided insights into the micro- and macro-level structure of literary texts. Combining these perspectives, in this work we study character networks extracted from a text corpus of J.R.R. Tolkien's Legendarium. We show that this perspective helps us to analyse and visualise the narrative style that characterises Tolkien's works. Addressing character classification, embedding and co-occurrence prediction, we further investigate the advantages of state-of-the-art Graph Neural Networks over a popular word embedding method. Our results highlight the large potential of graph learning in Computational Literary Studies.
