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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 Hits 1 – 10 of 10 1 Unsupervised Speech Unit Discovery Using K-means and Neural Networks Manenti, Céline; Pellegrini, Thomas; Pinquier, Julien In: SLSP 2017: Statistical Language and Speech Processing ; 5th International Conference on Statistical Language and Speech Processing (SLSP 2017) ; https://hal.archives-ouvertes.fr/hal-02559766 ; 5th International Conference on Statistical Language and Speech Processing (SLSP 2017), Oct 2017, Le Mans, France. pp.169-180 (2017) BASE Show details 2 Comparison and Fine-grained Analysis of Sequence Encoders for Natural Language Processing Keller, Thomas Anderson. - : eScholarship, University of California, 2017 In: Keller, Thomas Anderson. (2017). Comparison and Fine-grained Analysis of Sequence Encoders for Natural Language Processing. UC San Diego: Computer Science. Retrieved from: http://www.escholarship.org/uc/item/0wg0r7hn (2017) Abstract: Most machine learning algorithms require a fixed length input to be able to perform commonly desired tasks such as classification, clustering, and regression. For natural language processing, the inherently unbounded and recursive nature of the input poses a unique challenge when deriving such fixed length representations. Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning, the same cannot be said for sequences of words in sentences, paragraphs, or documents. In this work, we study the encoders commonly used to generate fixed length representations of natural language sequences, and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering. Additionally, we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks. Ultimately, we show that the setting in which the encoders are trained, and the corpus used for training, have a greater influence of the final learned representation than the underlying sequence encoders themselves. Keyword: Artificial intelligence; Computer science; Embedding; Linguistics; Natural Language Processing; Neural Networks; Question Answering; Sentence Representations; Unsupervised Learning URL: http://www.escholarship.org/uc/item/0wg0r7hn BASE Hide details 3 LiStr: Linguistic Structure Induction Tookit Mareček, David; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 4 Multilingual Bottle-Neck Feature Learning From Untranscribed Data For Track 1 In Zerospeech2017 (System 1 -- Without Vtln) ... Hongjie Chen; Cheung-Chi Leung; Xie, Lei. - : Zenodo, 2017 BASE Show details 5 Multilingual Bottle-Neck Feature Learning From Untranscribed Data For Track 1 In Zerospeech2017 (System 1 -- Without Vtln) ... Hongjie Chen; Cheung-Chi Leung; Xie, Lei. - : Zenodo, 2017 BASE Show details 6 Unsupervised neural and Bayesian models for zero-resource speech processing Kamper, Herman. - : The University of Edinburgh, 2017 BASE Show details 7 Unsupervised learning of allomorphs in Turkish Can, Burcu In: 25 ; 4 ; 3253 ; 3260 (2017) BASE Show details 8 Modeling morpheme triplets with a three-level hierarchical Dirichlet process Kumyol, Serkan; Can, Burcu In: 366 ; 369 (2017) BASE Show details 9 Computational Learning of Morphology Xanthos, Aris; Lee, Jackson L.; Goldsmith, John A. In: Annual Review of Linguistics, vol. 3, no. 1, pp. 85-106 (2017) BASE Show details 10 An unsupervised multilingual approach for online social media topic identification Chiong, Raymond; Lo, Siaw Ling; Cornforth, David. - : Pergamon Press, 2017 BASE Show details

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