自动提取葡萄牙语小说角色社交网络，准确率超现有工具。
Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature
- 结合词性标注与启发式规则识别角色及共指关系
- 角色识别F1达94.1%，互动检测达75.9%
- 专为葡萄牙语小说设计，适合文学分析研究者
从虚构文学中自动识别角色及其互动是一项复杂任务，需结合多种自然语言处理技术，如命名实体识别（NER）和词性标注（POS）。然而，现有方法未针对角色社交网络构建优化，尤其在低资源语言中表现不佳，因缺乏人工标注数据。本文提出名为Taggus的自动化流程，用于提取葡萄牙语小说中的角色社交网络。结果表明，相比现成的先进工具（即开箱即用的NER工具和大语言模型ChatGPT），该流程通过结合词性标注与启发式规则，在角色识别与共指消解任务中达到平均94.1%的F1分数，在互动检测任务中达75.9%。相较现有工具，分别提升50.7%和22.3%。文中也指出改进方向，如关系类型识别；承认测试样本规模与范围有限。Taggus流程已公开，以促进葡萄牙语文学分析领域的发展。
原文摘要 · Abstract (English)
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
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