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Published on Jul 18, 2018in arXiv: Computation and Language
Shao-Yen Tseng6
Estimated H-index: 6
,
Brian R. Baucom21
Estimated H-index: 21
,
Panayiotis G. Georgiou19
Estimated H-index: 19
Unsupervised learning has been an attractive method for easily deriving meaningful data representations from vast amounts of unlabeled data. These representations, or embeddings, often yield superior results in many tasks, whether used directly or as features in subsequent training stages. However, the quality of the embeddings is highly dependent on the assumed knowledge in the unlabeled data and how the system extracts information without supervision. Domain portability is also very limited in...
Published on Jan 1, 2018in arXiv: Computation and Language
Prashanth Gurunath Shivakumar4
Estimated H-index: 4
,
Panayiotis G. Georgiou19
Estimated H-index: 19
Word vector representations are a crucial part of Natural Language Processing (NLP) and Human Computer Interaction. In this paper, we propose a novel word vector representation, Confusion2Vec, motivated from the human speech production and perception that encodes representational ambiguity. Humans employ both acoustic similarity cues and contextual cues to decode information and we focus on a model that incorporates both sources of information. The representational ambiguity of acoustics, which ...
1 Citations
Published on Jun 10, 2019in PeerJ 2.12
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