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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)
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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)
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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
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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
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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
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6	Unsupervised neural and Bayesian models for zero-resource speech processing
	Kamper, Herman. - : The University of Edinburgh, 2017
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7	Unsupervised learning of allomorphs in Turkish
	Can, Burcu
	In: 25 ; 4 ; 3253 ; 3260 (2017)
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8	Modeling morpheme triplets with a three-level hierarchical Dirichlet process
	Kumyol, Serkan; Can, Burcu
	In: 366 ; 369 (2017)
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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)
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10	An unsupervised multilingual approach for online social media topic identification
	Chiong, Raymond; Lo, Siaw Ling; Cornforth, David. - : Pergamon Press, 2017
	Abstract: Social media data can be valuable in many ways. However, the vast amount of content shared and the linguistic variants of languages used on social media are making it very challenging for high-value topics to be identified. In this paper, we present an unsupervised multilingual approach for identifying highly relevant terms and topics from the mass of social media data. This approach combines term ranking, localised language analysis, unsupervised topic clustering and multilingual sentiment analysis to extract prominent topics through analysis of Twitter's tweets from a period of time. It is observed that each of the ranking methods tested has their strengths and weaknesses, and that our proposed ‘Joint’ ranking method is able to take advantage of the strengths of the ranking methods. This ‘Joint’ ranking method coupled with an unsupervised topic clustering model is shown to have the potential to discover topics of interest or concern to a local community. Practically, being able to do so may help decision makers to gauge the true opinions or concerns on the ground. Theoretically, the research is significant as it shows how an unsupervised online topic identification approach can be designed without much manual annotation effort, which may have great implications for future development of expert and intelligent systems.
	Keyword: multilingual analysis; social media; topic identification; unsupervised learning
	URL: http://hdl.handle.net/1959.13/1349216
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