A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Learning contextualized semantics from co-occurring terms via a Siamese architecture
2016
Neural Networks
One of the biggest challenges in Multimedia information retrieval and understanding is to bridge the semantic gap by properly modeling concept semantics in context. The presence of out of vocabulary (OOV) concepts exacerbates this difficulty. To address the semantic gap issues, we formulate a problem on learning contextualized semantics from descriptive terms and propose a novel Siamese architecture to model the contextualized semantics from descriptive terms. By means of pattern aggregation
doi:10.1016/j.neunet.2016.01.004
pmid:26874967
fatcat:wzz5n7gvbzckhdpxbtirwnn73y