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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 Hits 21 – 38 of 38 21 Translate and label! An encoder-decoder approach for cross-lingual semantic role labeling Daza, Angel [Verfasser]; Frank, Anette [Verfasser]; Inui, Kentaro [Herausgeber]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2019 DNB Subject Category Language Show details 22 Cross-lingual semantic specialization via lexical relation induction Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019 BASE Show details 23 Do we really need fully unsupervised cross-lingual embeddings? Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : Association for Computational Linguistics, 2019 BASE Show details 24 Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions ... Yokoi, Sho; Kobayashi, Sosuke; Fukumizu, Kenji; Suzuki, Jun; Inui, Kentaro. - : arXiv, 2018 Abstract: In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alternative to pointwise mutual information (PMI). As well as deriving PMI from mutual information, we derive this new measure from the Hilbert--Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of PMI that allows various similarity metrics (e.g., sentence embeddings) to be plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast (linear in the size of the data) matrix calculations regardless of whether we use linear or nonlinear kernels. Empirically, in a dialogue response selection task, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is beneficial as a criterion of a data selection task for machine translation owing to its ... : Accepted by EMNLP 2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning stat.ML URL: https://dx.doi.org/10.48550/arxiv.1809.00800 https://arxiv.org/abs/1809.00800 BASE Hide details 25 Unsupervised Learning of Style-sensitive Word Vectors ... Akama, Reina; Watanabe, Kento; Yokoi, Sho. - : arXiv, 2018 BASE Show details 26 The mechanism of additive composition [<Journal>] Tian, Ran [Verfasser]; Okazaki, Naoaki [Sonstige]; Inui, Kentaro [Sonstige] DNB Subject Category Language Show details 27 Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization ... Sasaki, Akira; Hanawa, Kazuaki; Okazaki, Naoaki. - : arXiv, 2017 BASE Show details 28 An Attentive Neural Architecture for Fine-grained Entity Type Classification ... Shimaoka, Sonse; Stenetorp, Pontus; Inui, Kentaro. - : arXiv, 2016 BASE Show details 29 Advances in probabilistic and other parsing technologies Hahn, Udo (Mitarb.); Sornlertlamvanich, Virach (Mitarb.); Vijay-Shanker, K. (Mitarb.). - Dordrecht [u.a.] : Kluwer, 2000 BLLDB UB Frankfurt Linguistik Show details 30 Integration of speech and image understanding : proceedings ; 21 September 1999, Corfu, Greece Roy, Deb K. (Mitarb.); Inui, Kentaro (Mitarb.); Denzler, J. (Mitarb.). - Los Alamitos, Calif. [u.a.] : IEEE Computer Society, 1999 BLLDB UB Frankfurt Linguistik Show details 31 Articles - Selective Sampling for Example-based Word Sense Disambiguation Fujii, Atsushi; Inui, Kentaro; Tokunaga, Takenobu... In: Computational linguistics. - Cambridge, Mass. : MIT Press 24 (1998) 4, 573-598 OLC Linguistik Show details 32 Selective sampling for example-based word sense disambiguation Fujii, Atsushi; Inui, Kentaro; Tokunaga, Takenobu... In: Computational linguistics. - Cambridge, Mass. : MIT Press 24 (1998) 4, 573-597 BLLDB Show details 33 A New Probabilistic LR Language Model for Statistical Parsing Inui Kentaro; Virach Sornlertlamvanich; Tanaka Hozumi... In: ftp://ftp.cs.titech.ac.jp/lab/tanaka/papers/97/inui97a.ps.gz (1997) BASE Show details 34 Natural Language Analysis and Generation Technologies Tanaka Hozumi; Takenobu Tokunaga; K. G. Suresh... In: ftp://ftp.cs.titech.ac.jp/pub/TR/93/TR93-0028.ps.gz (1993) BASE Show details 35 Text Revision: A Model and Its Implementation Inui Kentaro; Takenobu Tokunaga; Tanaka Hozumi In: ftp://ftp.cs.titech.ac.jp/pub/TR/92/TR92-0011.ps.gz (1992) BASE Show details 36 An Empirical Study on Statistical Disambiguation of Japanese Dependency Structures Using a Lexically Sensitive Language Model Shirai Kiyoaki; Inui Kentaro; Tanaka Hozumi... In: http://galaga.jaist.ac.jp:8000/~kshirai/papers/shirai97d.pdf BASE Show details 37 An Empirical Evaluation on Statistical Parsing of Japanese Sentences using Lexical Association Statistics Shirai Kiyoaki; Inui Kentaro; Tokunaga Takenobu... In: http://www.aclweb.org/anthology-new/W/W98/W98-1510.pdf BASE Show details 38 Fine-grained Utterance Delimitation and Organization in Incremental Explanation Generation Inui Kentaro; Sakaniwa Katsuyuki; Ishizawa Hiroaki... In: ftp://ftp.cs.titech.ac.jp/lab/tanaka/papers/97/inui97d.ps.gz BASE Show details Page: 1 2

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