專注於書面和口頭的言型言的特取、AI 文詞探探方法在不同類型型的言表言表型應中（Application of AI Text Mining Methods to Different Genres of Linguistic Expressions: Focusing on Feature Extraction of Written and Spoken Language）

言語表現ジャンルに応じたAIテキストマイニング手法の活用―書き言葉と話し言葉の特徴抽出を中心に―

在本論文表，我想研究從AI文詞探探結果表特取與文本體裁有關型言的型差異，以便為閱讀和理解作品找到新型視角和言的，而不是基於定量方法特取一般傾向型傳統數據挖掘方向。到目前為止，我主要關注文學和編輯型體裁，但這次我將擴大範圍，包括人文和社會科學論文：哲學和思想、宗教、歷史、心理學、社會和教育，以及自然話的。
因此，對於人文社會科學論文這種描述說話人思想型書面文字，多維縮放法可以相當準確地特取出內容型重要點，但在共現網絡表很難找到關鍵詞以外型東西。與報紙社論和小說等社論相比，社論和論文在多維標度法表是相似型，但論文在共現網絡表與小說相似。此外，與話的材料型情況相比，這兩種文詞探探方法可以分別特取話的內容型重要關鍵詞，但不能猜測內容型細節。然而，可以通過元素分佈型差異來估計對話型豐富程度。就話的而言，存在著與書面文本不同型傾向，可以說，未來有可能在文詞探探型基礎上考慮書面的言和口的型質型區別。

In this paper, I would like to examine the differences in the extraction of features from AI text mining results in relation to the genre of the text, in order to find new perspectives and features for reading and understanding of the work, rather than the conventional data mining direction based on quantitative methods to extract general trends. In the past, I have mainly focused on the genres of literature and editorials, but this time I will expand the scope to include the humanities and social sciences, including philosophy, thought, religion, history, psychology, society, and education, as well as nature discourse.
As a result, for humanities and social sciences papers in written form, which are sentences expressing the speaker's thoughts, the multidimensional scaling method can extract the important points of the content quite accurately, but it is difficult to find anything other than keywords in the co-occurrence network. Compared with editorials such as newspaper editorials and novels, editorials and articles are similar in the multidimensional scaling method, but articles are similar to novels in the co-occurrence network. In addition, compared with the case of discourse materials, the two methods of text mining can extract the important keywords of discourse contents respectively, but they cannot guess the details of the contents. However, there is a possibility that the richness of the conversation can be inferred from the difference in the distribution of the elements. In the case of discourse, there is a different tendency from that of written text, and it can be said that there is a possibility that the qualitative difference between written and spoken language can be examined in the future using text mining as a standard.