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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 69 1 『現代日本語書き言葉均衡コーパス』出版書籍サンプルのNDC別語彙分布 加藤 祥; 浅原 正幸; Sachi KATO... In: https://ccd.ninjal.ac.jp/lrw2021.html (2021) BASE Show details 2 Word Delimitation Issues in UD Japanese Mai Omura; Aya Wakasa; Masayuki Asahara. - : Association for Computational Linguistics, 2021 BASE Show details 3 編集後記 浅原 正幸; Masayuki ASAHARA In: https://ccd.ninjal.ac.jp/lrw2021.html (2021) BASE Show details 4 Lower Perplexity is Not Always Human-Like Tatsuki Kuribayashi; Yohei Oseki; Takumi Ito. - : Association for Computational Linguistics, 2021 BASE Show details 5 ALICE++ : Adversarial Training for Robust and Effective Temporal Reasoning Lis Kanashiro Pereira; Fei Cheng; Masayuki Asahara. - : Association for Computational Linguistics, 2021 BASE Show details 6 『分類語彙表』に対する反対語情報付与 加藤 祥; 浅原 正幸; 森山 奈々美. - : 言語処理学会, 2021 BASE Show details 7 The Annotation of Antonym Information in the 'Word List by Semantic Principles' Sachi Kato; Masayuki Asahara; Nanami Moriyama. - : Association for Computational Linguistics, 2021 BASE Show details 8 『現代日本語書き言葉均衡コーパス』新聞記事情報を用いたジャンル別語彙分布 加藤 祥; 森山 奈々美; 浅原 正幸... In: https://ccd.ninjal.ac.jp/lrw2021.html (2021) BASE Show details 9 『現代日本語書き言葉均衡コーパス』書籍サンプルのNDC情報増補 : NDC情報を用いた随筆の抽出と文体調査 加藤 祥; 森山 奈々美; 浅原 正幸. - : 国立国語研究所, 2021 BASE Show details 10 自然言語処理 : 言語資源・意味解析（レクチャーシリーズ「人工知能の今」第6回） 松林 優一郎; 浅原 正幸; Yuichiroh Matsubayashi... In: https://www.ai-gakkai.or.jp/vol35_no1/ (2020) BASE Show details 11 Design of BCCWJ-EEG : Balanced Corpus with Human Electroencephalography Yohei Oseki; Masayuki Asahara. - : European Language Resources Association, 2020 BASE Show details 12 KOTONOHA : A Corpus Concordance System for Skewer-Searching NINJAL Corpora Teruaki Oka; Yuichi Ishimoto; Yutaka Yagi. - : European Language Resources Association, 2020 BASE Show details 13 Adversarial Training for Commonsense Inference Lis Pereira; Xiaodong Liu; Fei Cheng. - : Association for Computational Linguistics, 2020 BASE Show details 14 日本語における名詞句の情報構造と語順の相関についての統計的検討 宮内 拓也; 浅原 正幸; Takuya Miyauchi. - : 言語処理学会, 2020 BASE Show details 15 Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings Teruo Hirabayashi; Kanako Komiya; Masayuki Asahara. - : Association for Computational Linguistics, 2020 BASE Show details 16 Generation and Evaluation of Concept Embeddings Via Fine-Tuning Using Automatically Tagged Corpus Kanako Komiya; Daiki Yaginuma; Masayuki Asahara. - : Association for Computational Linguistics, 2020 BASE Show details 17 編集後記 浅原 正幸; Masayuki Asahara In: https://pj.ninjal.ac.jp/corpus_center/lrw2020.html (2020) BASE Show details 18 Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding Teruo Hirabayashi; Kanako Komiya; Masayuki Asahara. - : European Language Resources Association, 2020 BASE Show details 19 Bayesian Linear Mixed Model による単語親密度推定と位相情報付与 浅原 正幸; Masayuki Asahara. - : 言語処理学会, 2020 BASE Show details 20 Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning Fei Cheng; Masayuki Asahara; Ichiro Kobayashi; Sadao Kurohashi. - : Association for Computational Linguistics, 2020 Abstract: Kyoto University ; National Institute for Japanese Language and Linguistics ; Ochanomizu University ; Kyoto University ; Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two transfer learning baselines on both the English and Japanese data. URL: http://id.nii.ac.jp/1328/00003267/ https://repository.ninjal.ac.jp/?action=repository_action_common_download&item_id=3283&item_no=1&attribute_id=22&file_no=1 https://repository.ninjal.ac.jp/?action=repository_uri&item_id=3283 BASE Hide details Page: 1 2 3 4

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