论文标题
Dungeons＆Dragons的特定领域特定大数据集的合成和评估
Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons
论文作者
论文摘要
本文介绍了使用FRW以及相关分析的被遗忘的领域Wiki（FRW）数据集和域特定于自然语言的生成。被遗忘的领域是流行的开放式桌面幻想角色扮演游戏，Dungeons＆Dragons的事实默认设置。数据集是从被遗忘的领域狂热Wiki中提取的，该Wiki由超过45,200篇文章组成。 FRW数据集由多种格式的11个sub-data集组成：原始纯文本，由文章标题注释的纯文本，定向链接图，Wiki Info-boxes由Wiki Artist Artist Title，Poincaré嵌入第一个链接图的Poincaré嵌入，多个Word2Vec和Doc2vec模型。这是Dungeons＆Dragons域的此大小的第一个数据集。然后，我们提出了采用相似性度量的成对相似性比较基准。此外，我们使用语料库进行了D＆D领域的特定自然语言生成，并根据被遗忘的领域的知识评估了指定的实体分类。
This paper introduces the Forgotten Realms Wiki (FRW) data set and domain specific natural language generation using FRW along with related analyses. Forgotten Realms is the de-facto default setting of the popular open ended tabletop fantasy role playing game, Dungeons & Dragons. The data set was extracted from the Forgotten Realms Fandom wiki consisting of more than over 45,200 articles. The FRW data set is constituted of 11 sub-data sets in a number of formats: raw plain text, plain text annotated by article title, directed link graphs, wiki info-boxes annotated by the wiki article title, Poincaré embedding of first link graph, multiple Word2Vec and Doc2Vec models of the corpus. This is the first data set of this size for the Dungeons & Dragons domain. We then present a pairwise similarity comparison benchmark which utilizes similarity measures. In addition, we perform D&D domain specific natural language generation using the corpus and evaluate the named entity classification with respect to the lore of Forgotten Realms.
