标题： Taggus：从葡萄牙小说文学中提取角色社交网络的自动化流程 显示英文标题

标题： Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature

摘要： 从小说书籍中自动识别角色及其互动，可以说是一个复杂的任务，需要利用多种自然语言处理（NLP）方法的管道，例如命名实体识别（NER）和词性（POS）标注。 然而，这些方法并不是为导致角色社交网络构建的任务而优化的。 事实上，目前可用的方法表现不佳，特别是在代表性不足的语言中，这是由于缺乏用于训练的手动注释数据。 在此，我们提出一个管道，称为Taggus，以从葡萄牙语文学小说作品中提取社交网络。 我们的结果表明，与现成的最先进工具——现成的NER工具和大型语言模型（ChatGPT）相比，该使用POS标注和启发式方法组合的管道在识别角色和解决共指问题的任务中取得了令人满意的结果，平均F1分数为$94.1\%$，并在互动检测中为$75.9\%$。 这分别比现成的最先进工具取得的结果提高了$50.7\%$和$22.3\%$。 进一步的步骤以提高结果已被概述，例如检测角色之间关系的解决方案。 承认了我们在测试样本大小和范围上的限制。 Taggus管道是公开的，以鼓励在葡萄牙语领域的这一领域的发展。 显示英文摘要

摘要： Automatically identifying characters and their interactions from fiction books is, arguably, a complex task that requires pipelines that leverage multiple Natural Language Processing (NLP) methods, such as Named Entity Recognition (NER) and Part-of-speech (POS) tagging. However, these methods are not optimized for the task that leads to the construction of Social Networks of Characters. Indeed, the currently available methods tend to underperform, especially in less-represented languages, due to a lack of manually annotated data for training. Here, we propose a pipeline, which we call Taggus, to extract social networks from literary fiction works in Portuguese. Our results show that compared to readily available State-of-the-Art tools -- off-the-shelf NER tools and Large Language Models (ChatGPT) -- the resulting pipeline, which uses POS tagging and a combination of heuristics, achieves satisfying results with an average F1-Score of $94.1\%$ in the task of identifying characters and solving for co-reference and $75.9\%$ in interaction detection. These represent, respectively, an increase of $50.7\%$ and $22.3\%$ on results achieved by the readily available State-of-the-Art tools. Further steps to improve results are outlined, such as solutions for detecting relationships between characters. Limitations on the size and scope of our testing samples are acknowledged. The Taggus pipeline is publicly available to encourage development in this field for the Portuguese language.2