@techreport{oai:ipsj.ixsq.nii.ac.jp:00199631,
 author = {大久保, 梨思子 and 倉林, 翔 and 森, 健史 and 波平, 晃佑 and 植田, 泰士 and 片平, 真史 and 天笠, 俊之 and Naoko, Okubo and Sho, Kurahayashi and Kenji, Mori and Kousuke, Namihira and Yasushi, Ueda and Masafumi, Katahira and Toshiyuki, Amagasa},
 issue = {24},
 month = {Sep},
 note = {本稿では，word2vec で学習した単語の分散表現モデルの質について，実タスクへの適用の前に評価する方法の検討結果を報告する．分散表現モデルの学習結果の良し悪しは，実タスクへ適用した際に確認可能である． しかし，宇宙機開発等の専門知識を要する評価が必要なタスクへ分散表現モデルを適用した場合，評価可能な者が限定されるため評価コストを複数回かけることが難しい．そこで，分散表現モデルの良し悪しを，実タスクへの適用前にある程度の精度で比較評価する方法を検討した．また，分散表現モデルの評価結果と実タスクの精度の相関を調査した．, In this paper, we report the examination result of the method to evaluate the quality of the word-embedding models with word2vec before applying them to the actual NLP task. The quality about word-embedding models can be confirmed when applied to a real NLP task. However, when the word-embedding model is applied to tasks that require specialized knowledge for the evaluation, such as spacecraft development, it is difficult to apply multiple evaluation costs because the number of people who can be evaluated is limited. Therefore, we examined　a method to evaluate the quality of the word-embedding models with a certain degree of accuracy before applying　it to a real NLP task. We also investigated the correlation between the evaluation result of the word-embedding model and the accuracy of the actual NLP task.},
 title = {宇宙機関連テキストデータの分散表現モデルの比較手法の検討},
 year = {2019}
}