Bilingual Data Selection Using a Continuous Vector-Space Representation

2016 ◽  
pp. 95-106
Author(s):  
Mara Chinea-Rios ◽  
Germán Sanchis-Trilles ◽  
Francisco Casacuberta
Keyword(s):  
Vector Space ◽  
Data Selection ◽  
Space Representation ◽  
Continuous Vector ◽  
Vector Space Representation

Story Cloze Evaluator: Vector Space Representation Evaluation by Predicting What Happens Next

2016 ◽  
Author(s):  
Nasrin Mostafazadeh ◽  
Lucy Vanderwende ◽  
Wen-tau Yih ◽  
Pushmeet Kohli ◽  
James Allen
Keyword(s):  
Vector Space ◽  
Space Representation ◽  
Vector Space Representation

scholarly journals Multidimensional Vector Space Representation for Convergent Evolution and Molecular Phylogeny

2004 ◽  
Vol 22 (3) ◽  
pp. 704-715 ◽  
Author(s):  
Yasuhiro Kitazoe ◽  
Hirohisa Kishino ◽  
Takahisa Okabayashi ◽  
Teruaki Watabe ◽  
Noriaki Nakajima ◽  
...  
Keyword(s):  
Molecular Phylogeny ◽  
Vector Space ◽  
Convergent Evolution ◽  
Space Representation ◽  
Vector Space Representation

On the Employment of SMI Beamforming for Cochannel Interference Mitigation in Digital Radio

2009 ◽  
pp. 82-93
Author(s):  
Thomas Hunziker
Keyword(s):  
Vector Space ◽  
Interference Mitigation ◽  
Coded Modulation ◽  
Cochannel Interference ◽  
Space Representation ◽  
Soft Decision ◽  
Definition Of ◽  
Short Signal ◽  
Cochannel Interference Mitigation ◽  
Vector Space Representation

Many common adaptive beamforming methods are based on a sample matrix inversion (SMI). The schemes can be applied in two ways. The sample covariance matrices are either computed over preambles, or the sample basis for the SMI and the target of the beamforming are identical. A vector space representation provides insight into the classic SMI-based beamforming variants, and enables elegant derivations of the well-known second-order statistical properties of the output signals. Moreover, the vector space representation is helpful in the definition of appropriate interfaces between beamfoming and soft-decision signal decoding in receivers aiming at adaptive cochannel interference mitigation. It turns out that the performance of standard receivers incorporating SMI-based beamforming on short signal intervals and decoding of BICM (bit-interleaved coded modulation) signals can be significantly improved by proper interface design.

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