SHADE：面向领域特定实体的语义上位词标注器——以龙与地下城领域为用例
SHADE: Semantic Hypernym Annotator for Domain-specific Entities -- DnD Domain Use Case
AI总结：
本文提出SHADE标注软件，针对龙与地下城等高度奇幻文学领域，通过预定义标签和约束机制解决实体标注成本高、一致性差及人为错误多的问题。
摘要：
人工数据标注是一项重要的自然语言处理任务，但需要耗费大量资源和精力。尽管成本高昂，对实体进行标记和分类对于语义评估等自然语言处理任务仍然必不可少。虽然在大多数情况下标注可以由非专家完成，但由于这一过程需要人力劳动，因此成本较高。数据标注中遇到的另一个主要挑战是保持标注一致性。标注工作通常由多名标注者组成的团队执行。标注需要在领域事实和标注格式两方面保持一致，同时减少人为错误。对与通用领域显著偏离的专门领域（例如奇幻文学）进行标注时，会出现大量人为错误和标注者分歧。因此，必须落实适当的指导规范和错误减少机制。实施这些约束的一种方式是使用专门的应用程序。此类应用可以确保标注符号的一致性，并且标签可以预先定义或加以限制，从而减少出错空间。在本文中，我们提出了SHADE，这是一款标注软件，可用于对高度奇幻文学领域中的实体进行标注，具体针对从被遗忘的国度Fandom Wiki中提取的龙与地下城传说内容。
英文摘要：
Manual data annotation is an important NLP task but one that takes considerable amount of resources and effort. In spite of the costs, labeling and categorizing entities is essential for NLP tasks such as semantic evaluation. Even though annotation can be done by non-experts in most cases, due to the fact that this requires human labor, the process is costly. Another major challenge encountered in data annotation is maintaining the annotation consistency. Annotation efforts are typically carried out by teams of multiple annotators. The annotations need to maintain the consistency in relation to both the domain truth and annotation format while reducing human errors. Annotating a specialized domain that deviates significantly from the general domain, such as fantasy literature, will see a lot of human error and annotator disagreement. So it is vital that proper guidelines and error reduction mechanisms are enforced. One such way to enforce these constraints is using a specialized application. Such an app can ensure that the notations are consistent, and the labels can be pre-defined or restricted reducing the room for errors. In this paper, we present SHADE, an annotation software that can be used to annotate entities in the high fantasy literature domain. Specifically in Dungeons and Dragons lore extracted from the Forgotten Realms Fandom Wiki.
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。
