| 000 | 05042naaaa2201501uu 4500 | ||
|---|---|---|---|
| 003 | BUT | ||
| 005 | 20230403172307.0 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20220506s2022 xx |||||o ||| eng|| d | ||
| 020 | _a9783036522715 | ||
| 020 | _a9783036522722 | ||
| 040 |
_aoapen _coapen |
||
| 041 | 0 | _aeng | |
| 080 | _a004 | ||
| 100 | 1 |
_aEsposito, Massimo _4edt |
|
| 245 | 1 | 0 | _aNatural Language Processing: Emerging Neural Approaches and Applications |
| 260 |
_aBasel _bMDPI - Multidisciplinary Digital Publishing Institute _c2022 |
||
| 300 | _a1 electronic resource (544 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aThis Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc |
||
| 546 | _aEnglish | ||
| 650 | 0 |
_aКомпьютерная инженерия _94537 |
|
| 653 | _atourism big data | ||
| 653 | _atext mining | ||
| 653 | _aNLP | ||
| 653 | _adeep learning | ||
| 653 | _aclinical named entity recognition | ||
| 653 | _ainformation extraction | ||
| 653 | _amultitask model | ||
| 653 | _along short-term memory | ||
| 653 | _aconditional random field | ||
| 653 | _arelation extraction | ||
| 653 | _aentity recognition | ||
| 653 | _along short-term memory network | ||
| 653 | _amulti-turn chatbot | ||
| 653 | _adialogue context encoding | ||
| 653 | _aWGAN-based response generation | ||
| 653 | _aBERT word embedding | ||
| 653 | _atext summary | ||
| 653 | _areinforce learning | ||
| 653 | _aFAQ classification | ||
| 653 | _aencoder-decoder neural network | ||
| 653 | _amulti-level word embeddings | ||
| 653 | _aBERT | ||
| 653 | _abidirectional RNN | ||
| 653 | _acloze test | ||
| 653 | _aKorean dataset | ||
| 653 | _amachine comprehension | ||
| 653 | _aneural language model | ||
| 653 | _asentence completion | ||
| 653 | _aprimary healthcare | ||
| 653 | _achief complaint | ||
| 653 | _avirtual medical assistant | ||
| 653 | _aspoken natural language | ||
| 653 | _adisease diagnosis | ||
| 653 | _amedical specialist | ||
| 653 | _aprotein–protein interactions | ||
| 653 | _adeep learning (DL) | ||
| 653 | _aconvolutional neural networks (CNN) | ||
| 653 | _abidirectional long short-term memory (bidirectional LSTM) | ||
| 653 | _adialogue management | ||
| 653 | _auser simulation | ||
| 653 | _areward shaping | ||
| 653 | _aconversation knowledge | ||
| 653 | _amulti-agent reinforcement learning | ||
| 653 | _alanguage modeling | ||
| 653 | _aclassification | ||
| 653 | _aerror probability | ||
| 653 | _aerror assessment | ||
| 653 | _alogic error | ||
| 653 | _aneural network | ||
| 653 | _aLSTM | ||
| 653 | _aattention mechanism | ||
| 653 | _aprogramming education | ||
| 653 | _aneural architecture search | ||
| 653 | _aword ordering | ||
| 653 | _aKorean syntax | ||
| 653 | _aadversarial attack | ||
| 653 | _aadversarial example | ||
| 653 | _asentiment classification | ||
| 653 | _adual pointer network | ||
| 653 | _acontext-to-entity attention | ||
| 653 | _atext classification | ||
| 653 | _arule-based | ||
| 653 | _aword embedding | ||
| 653 | _aDoc2vec | ||
| 653 | _aparaphrase identification | ||
| 653 | _aencodings | ||
| 653 | _aR-GCNs | ||
| 653 | _acontextual features | ||
| 653 | _asentence retrieval | ||
| 653 | _aTF−ISF | ||
| 653 | _aBM25 | ||
| 653 | _apartial match | ||
| 653 | _asequence similarity | ||
| 653 | _aword to vector | ||
| 653 | _aword embeddings | ||
| 653 | _aantonymy detection | ||
| 653 | _apolarity | ||
| 653 | _atext normalization | ||
| 653 | _anatural language processing | ||
| 653 | _adeep neural networks | ||
| 653 | _acausal encoder | ||
| 653 | _aquestion classification | ||
| 653 | _amultilingual | ||
| 653 | _aconvolutional neural networks | ||
| 653 | _aNatural Language Processing (NLP) | ||
| 653 | _atransfer learning | ||
| 653 | _aopen information extraction | ||
| 653 | _arecurrent neural networks | ||
| 653 | _abilingual translation | ||
| 653 | _aspeech-to-text | ||
| 653 | _aLaTeX decompilation | ||
| 653 | _aword representation learning | ||
| 700 | 1 |
_aMasala, Giovanni Luca _4edt |
|
| 700 | 1 |
_aMinutolo, Aniello _4edt |
|
| 700 | 1 |
_aPota, Marco _4edt |
|
| 700 | 1 |
_aEsposito, Massimo _4oth |
|
| 700 | 1 |
_aMasala, Giovanni Luca _4oth |
|
| 700 | 1 |
_aMinutolo, Aniello _4oth |
|
| 700 | 1 |
_aPota, Marco _4oth |
|
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/5219 _70 _zDownload |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/81187 _70 _zDescription |
| 909 |
_c4 _dDarya Shvetsova |
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| 942 |
_2udc _cEE |
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| 999 |
_c6313 _d6313 |
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