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pro vyhledávání: '"Qin, Kechen"'
Autor:
Qin, Kechen, Wang, Yu, Li, Cheng, Gunaratna, Kalpa, Jin, Hongxia, Pavlu, Virgil, Aslam, Javed A.
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them are trained based on labeled reasoning path. This hinders the system's
Externí odkaz:
http://arxiv.org/abs/2005.10970
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and insta
Externí odkaz:
http://arxiv.org/abs/1904.05937
We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence. Previous such RNN models define probabilities for sequences but not for sets; attempts to obta
Externí odkaz:
http://arxiv.org/abs/1904.05829
We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent
Externí odkaz:
http://arxiv.org/abs/1705.05039
Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has t
Externí odkaz:
http://arxiv.org/abs/1705.05040
Akademický článek
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Publikováno v:
2014 International Conference on Identification, Information & Knowledge in the Internet of Things; 2014, p233-236, 4p
Publikováno v:
2014 International Conference on Identification, Information & Knowledge in the Internet of Things; 2014, p206-211, 6p