数字人文领域分布语义任务数据集的创建与评估
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
AI总结：
本文针对数字人文领域，基于两部奇幻小说系列训练并比较多种词嵌入模型，联合专家构建术语类比与词语侵入数据集，证明小语料嵌入亦可表现良好。
摘要：
词嵌入在通用领域已得到充分研究，通常在大型文本语料库上训练，并已在例如词语相似度和类比任务上接受评估，同时也被用作下游NLP流程的输入。相比之下，本研究探索词嵌入技术在专门的数字人文领域中的适用性。我们在两部流行的奇幻小说系列上训练了多种类型的嵌入模型，随后评估其在两类任务上的表现：术语类比和词语侵入。为此，我们与领域专家共同手工构建了测试数据集。贡献之一是在不同任务类型上评估多种词嵌入技术，并发现即使是在小型语料库上训练的嵌入，也能在例如词语侵入任务上取得良好表现。此外，我们提供了数字人文领域中用于进一步研究的大量高质量数据集，以及便于轻松复现或扩展实验的实现代码。
英文摘要：
Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but also as an input to downstream NLP processes. In contrast, in this work we explore the suitability of word embedding technologies in the specialized digital humanities domain. After training embedding models of various types on two popular fantasy novel book series, we evaluate their performance on two task types: term analogies, and word intrusion. To this end, we manually construct test datasets with domain experts. Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task. Furthermore, we provide extensive and high-quality datasets in digital humanities for further investigation, as well as the implementation to easily reproduce or extend the experiments.
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。
