Cite this DOI

Krishna, S. M., & Mohan Pattaanayak, R. (2017). A Three Layer Stacked Auto Encoders for Semantic Hashing. *Journal of Science & Technology*, *02*(04), 49–51. https://doi.org/10.46243/jstj.2017.v2.i4.213

@article{krishna2017three,
  author    = {Krishna, Sadi Mohan and Mohan Pattaanayak, Radha},
  title     = {{A Three Layer Stacked Auto Encoders for Semantic Hashing}},
  journal   = {Journal of Science \& Technology},
  year      = {2017},
  month     = {jul},
  volume    = {02},
  number    = {04},
  pages     = {49--51},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jstj.2017.v2.i4.213},
  url       = {https://doi.org/10.46243/jstj.2017.v2.i4.213},
  language  = {en},
  abstract  = {Suggesting an original method for sympathetic short texts is to present a device to augment short texts with thoughts and co-occurring terms that are mined from a probabilistic semantic network. To bunch short texts by their senses, we suggest to add more semantic signals to short texts. Precisely, for each term in a short text, we get its concepts and co-occurring terms from a probabilistic information base to augment the short text. Also, we present a basic bottomless learning network entailing of a 3-layer stacked auto-encoders for semantic hashing.Keywords:semantic enrichment, semantic hashing, deep neural network}
}

TY  - JOUR
TI  - A Three Layer Stacked Auto Encoders for Semantic Hashing
AU  - Krishna, Sadi Mohan
AU  - Mohan Pattaanayak, Radha
JO  - Journal of Science & Technology
PY  - 2017
DA  - 2017/07/15/
VL  - 02
IS  - 04
SP  - 49
EP  - 51
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Suggesting an original method for sympathetic short texts is to present a device to augment short texts with thoughts and co-occurring terms that are mined from a probabilistic semantic network. To bunch short texts by their senses, we suggest to add more semantic signals to short texts. Precisely, for each term in a short text, we get its concepts and co-occurring terms from a probabilistic information base to augment the short text. Also, we present a basic bottomless learning network entailing of a 3-layer stacked auto-encoders for semantic hashing.Keywords:semantic enrichment, semantic hashing, deep neural network
DO  - 10.46243/jstj.2017.v2.i4.213
UR  - https://doi.org/10.46243/jstj.2017.v2.i4.213
ER  -

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    "DOI": "10.46243/jstj.2017.v2.i4.213",
    "URL": "https://doi.org/10.46243/jstj.2017.v2.i4.213",
    "title": "A Three Layer Stacked Auto Encoders for Semantic Hashing",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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    "volume": "02",
    "issue": "04",
    "page": "49-51",
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    "language": "en",
    "abstract": "Suggesting an original method for sympathetic short texts is to present a device to augment short texts with thoughts and co-occurring terms that are mined from a probabilistic semantic network. To bunch short texts by their senses, we suggest to add more semantic signals to short texts. Precisely, for each term in a short text, we get its concepts and co-occurring terms from a probabilistic information base to augment the short text. Also, we present a basic bottomless learning network entailing of a 3-layer stacked auto-encoders for semantic hashing.Keywords:semantic enrichment, semantic hashing, deep neural network",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjstj.2017.v2.i4.213 gives all four in one JSON answer.