no code implementations • RANLP 2021 • Youki Itoh, Hiroyuki Shinnou
Herein, we propose a method for addressing the computational efficiency of pretraining models in domain shift by constructing an ELECTRA pretraining model on a Japanese dataset and additional pretraining this model in a downstream task using a corpus from the target domain.
no code implementations • RANLP 2021 • Naoki Kikuta, Hiroyuki Shinnou
In an experiment using the livedoor news corpus, which is Japanese, we compared the accuracy of document classification using two methods for selecting documents to be concatenated with that of ordinary document classification.
no code implementations • LREC 2020 • Teruo Hirabayashi, Kanako Komiya, Masayuki Asahara, Hiroyuki Shinnou
However, because our method utilized the embedding vectors of the word senses, the relations of the sense tags corresponding to concept tags could be examined by mapping the sense embeddings to the vector space of the concept tags.
no code implementations • WS 2018 • Kanako Komiya, Hiroyuki Shinnou
The experiments revealed that fine-tuning sometimes give adverse effect when only a small target corpus is used and batch size is the most important parameter for fine-tuning.
no code implementations • LREC 2012 • Minoru Sasaki, Hiroyuki Shinnou
Then, peculiar examples are extracted using the local outlier factor, which is a density-based outlier detection method, from the updated training and test data.