论文标题
使用子词单元提取日本虚构字符的语言语音模式
Extracting linguistic speech patterns of Japanese fictional characters using subword units
论文作者
论文摘要
这项研究提取并分析了日本动漫或游戏角色的语言语音模式。传统的形态分析仪，例如mecab，具有高性能的段单词，但它们无法分割词典中未列出的折断表达式或话语结尾，这些词通常以动漫或游戏角色出现。为了克服这一挑战，我们建议使用主要用于深度学习的子词单元进行分割日本动漫或游戏角色的细分线，并提取经常出现的字符串以获得表现出其话语的表达方式。我们根据性别，年龄和每个动漫角色分析了由TF/IDF加权的子单词单元，并表明它们是针对每个功能的语言语音模式。此外，一个分类实验表明，具有子词单元的模型用常规方法优于该模型。
This study extracted and analyzed the linguistic speech patterns that characterize Japanese anime or game characters. Conventional morphological analyzers, such as MeCab, segment words with high performance, but they are unable to segment broken expressions or utterance endings that are not listed in the dictionary, which often appears in lines of anime or game characters. To overcome this challenge, we propose segmenting lines of Japanese anime or game characters using subword units that were proposed mainly for deep learning, and extracting frequently occurring strings to obtain expressions that characterize their utterances. We analyzed the subword units weighted by TF/IDF according to gender, age, and each anime character and show that they are linguistic speech patterns that are specific for each feature. Additionally, a classification experiment shows that the model with subword units outperformed that with the conventional method.
