著者
河野 慎 植田 一博
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.32, no.1, pp.WII-E_1-8, 2017-01-06 (Released:2017-01-20)
参考文献数
23

People collect and use information about real world from internet to help their daily activities. In particular, the number of users in microblog such as Twitter is so large that users can get a diversity of information. They can elicit not only the information which they need from microblog posts but also the location which is indicated by the contents posted in microblog. While previous approaches apply corpus-based or machine learning that require various prior knowledge such as natural language processing and feature engineering, our approach is able to estimate the location without those requirements with extension of long-short term memory (LSTM). In our experiment, we apply our approach to geo-tagged tweets posted in Twitter and show that this approach is effective in outperforming corpus-based and previous works that use support vector machine (SVM) with bag-of-words (BoW).

言及状況

外部データベース (DOI)

Twitter (2 users, 2 posts, 1 favorites)

河野慎・植田一博(2017). Recurrent Neural Networkによるマイクロブログの投稿位置推定 人工知能学会論文誌 32, WII-E_1-8. https://t.co/uDCgUJXxHD
Recurrent Neural Networkによるマイクロブログの投稿位置推定 https://t.co/vgzC2OOhAF

収集済み URL リスト