no code implementations • NAACL (SIGTYP) 2021 • Ran Iwamoto, Hiroshi Kanayama, Alexandre Rademaker, Takuya Ohko
This paper investigates updates of Universal Dependencies (UD) treebanks in 23 languages and their impact on a downstream application.
no code implementations • LREC 2022 • Yang Zhao, Hiroshi Kanayama, Issei Yoshida, Masayasu Muraoka, Akiko Aizawa
To remedy this shortcoming, we present a dependency-tree-based method to construct a Chinese corpus with 151k pairs of sentences and compression based on Chinese language-specific characteristics.
no code implementations • 31 Jan 2023 • Takuma Udagawa, Hiroshi Kanayama, Issei Yoshida
To tackle this issue, we formulate a novel task of sentence identification, where the goal is to identify SUs while excluding NSUs in a given text.
1 code implementation • 12 Oct 2022 • Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li
In this paper, we address key practical issues with existing evaluation scripts and propose a more strict SRL evaluation metric PriMeSRL.
no code implementations • COLING 2020 • Yousef El-Kurdi, Hiroshi Kanayama, Efsun Sarioglu Kayi, Vittorio Castelli, Todd Ward, Radu Florian
We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text.
no code implementations • ACL 2020 • Ryosuke Kohita, Issei Yoshida, Hiroshi Kanayama, Tetsuya Nasukawa
We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis.
no code implementations • LREC 2020 • Hiroshi Kanayama, Ran Iwamoto
This paper investigates clause-level sentiment detection in a multilingual scenario.
no code implementations • WS 2018 • Hiroshi Kanayama, Na-Rae Han, Masayuki Asahara, Jena D. Hwang, Yusuke Miyao, Jinho D. Choi, Yuji Matsumoto
This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean.
no code implementations • WS 2018 • Hiroshi Kanayama, Masayasu Muraoka, Ryosuke Kohita
This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese.
no code implementations • CONLL 2017 • Daniel Zeman, Martin Popel, Milan Straka, Jan Haji{\v{c}}, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkov{\'a}, Jan Haji{\v{c}} jr., Jaroslava Hlav{\'a}{\v{c}}ov{\'a}, V{\'a}clava Kettnerov{\'a}, Zde{\v{n}}ka Ure{\v{s}}ov{\'a}, Jenna Kanerva, Stina Ojala, Anna Missil{\"a}, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria de Paiva, Kira Droganova, H{\'e}ctor Mart{\'\i}nez Alonso, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, M, Michael l, Jesse Kirchner, Hector Fern Alcalde, ez, Jana Strnadov{\'a}, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendon{\c{c}}a, L, Tatiana o, Rattima Nitisaroj, Josie Li
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
no code implementations • CONLL 2017 • Hiroshi Kanayama, Masayasu Muraoka, Katsumasa Yoshikawa
This paper presents our system submitted for the CoNLL 2017 Shared Task, {``}Multilingual Parsing from Raw Text to Universal Dependencies.
no code implementations • EACL 2017 • Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, Trevor Cohn
Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer.
1 code implementation • EMNLP 2016 • Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, Trevor Cohn
Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools.
Bilingual Lexicon Induction Cross-Lingual Document Classification +4
no code implementations • LREC 2016 • Takaaki Tanaka, Yusuke Miyao, Masayuki Asahara, Sumire Uematsu, Hiroshi Kanayama, Shinsuke Mori, Yuji Matsumoto
We present an attempt to port the international syntactic annotation scheme, Universal Dependencies, to the Japanese language in this paper.