扩展事件类型本体：利用微调大语言模型建议添加动词与类别
Extending an Event-type Ontology: Adding Verbs and Classes Using Fine-tuned LLMs Suggestions
AI总结：
本文研究利用微调大语言模型为事件类型本体的词汇扩展任务进行动词预标注，通过自动评分辅助标注者识别新类别种子，并验证了自动评分与人工标注的强相关性及对标注效率的积极影响。
摘要：
在本项目中，我们研究了使用先进的机器学习方法，特别是经过微调的大语言模型，为一个词汇扩展任务进行数据预标注，即向一个现有的（但截至目前仍不完整的）事件类型本体中添加描述性词语（动词）。我们聚焦了若干研究问题，从探索一种可能的启发式方法以至少为标注者提供提示——哪些动词应被纳入、哪些在当前版本的本体之外，到可能利用自动评分来帮助标注者更高效地找到阈值，以识别无法归入任何现有类别的动词，从而将其用作新类别的种子。我们还仔细考察了自动评分与人工标注之间的相关性。虽然相关性被证明很强，但由于其接近线性，它对标注本身的影响较为有限，尽管这种预标注的存在本身就使得标注时间相对较短。
英文摘要：
In this project, we have investigated the use of advanced machine learning methods, specifically fine-tuned large language models, for pre-annotating data for a lexical extension task, namely adding descriptive words (verbs) to an existing (but incomplete, as of yet) ontology of event types. Several research questions have been focused on, from the investigation of a possible heuristics to provide at least hints to annotators which verbs to include and which are outside the current version of the ontology, to the possible use of the automatic scores to help the annotators to be more efficient in finding a threshold for identifying verbs that cannot be assigned to any existing class and therefore they are to be used as seeds for a new class. We have also carefully examined the correlation of the automatic scores with the human annotation. While the correlation turned out to be strong, its influence on the annotation proper is modest due to its near linearity, even though the mere fact of such pre-annotation leads to relatively short annotation times.
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。
