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pro vyhledávání: '"Higashiyama, Shohei"'
Autor:
Yamamoto, Aitaro, Otomo, Hiroyuki, Ouchi, Hiroki, Higashiyama, Shohei, Teranishi, Hiroki, Shindo, Hiroyuki, Watanabe, Taro
Previous studies on sequence-based extraction of human movement trajectories have an issue of inadequate trajectory representation. Specifically, a pair of locations may not be lined up in a sequence especially when one location includes the other ge
Externí odkaz:
http://arxiv.org/abs/2410.16633
Autor:
Higashiyama, Shohei, Ouchi, Hiroki, Teranishi, Hiroki, Otomo, Hiroyuki, Ide, Yusuke, Yamamoto, Aitaro, Shindo, Hiroyuki, Matsuda, Yuki, Wakamiya, Shoko, Inoue, Naoya, Yamada, Ikuya, Watanabe, Taro
Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for evalu
Externí odkaz:
http://arxiv.org/abs/2305.13844
Autor:
Ouchi, Hiroki, Shindo, Hiroyuki, Wakamiya, Shoko, Matsuda, Yuki, Inoue, Naoya, Higashiyama, Shohei, Nakamura, Satoshi, Watanabe, Taro
We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research. This dataset is a Japanese text dataset with a total of over 31 million words, comprising 4,672 Japanese domestic travelogues and 9,607 overseas tr
Externí odkaz:
http://arxiv.org/abs/2305.11444
Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comp
Externí odkaz:
http://arxiv.org/abs/2104.03523
Autor:
Dhar, Prajit, Bisazza, Arianna, van Noord, Gertjan, Nakazawa, Toshiaki, Nakayama, Hideki, Goto, Isao, Mino, Hideya, Ding, Chenchen, Dabre, Raj, Kunchukuttan, Anoop, Higashiyama, Shohei, Manabe, Hiroshi, Pa Pa, Win, Parida, Shantipriya, Bojar, Ondřej, Chu, Chenhui, Eriguchi, Akiko, Abe, Kaori, Oda, Yusuke, Sudoh, Katsuhito, Kurohashi, Sadao, Bhattacharyya, Pushpak
Publikováno v:
Proceedings of the 8th Workshop on Asian Translation (WAT2021), 181-190
STARTPAGE=181;ENDPAGE=190;TITLE=Proceedings of the 8th Workshop on Asian Translation (WAT2021)
STARTPAGE=181;ENDPAGE=190;TITLE=Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::cb43ea02139636b6518a06f0cb0da06c
https://research.rug.nl/en/publications/0d4b6254-b565-4673-a48e-00a98e4adb54
https://research.rug.nl/en/publications/0d4b6254-b565-4673-a48e-00a98e4adb54
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