Deep Learning applied to NLP
Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten s...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2017-03 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Marc Moreno Lopez Kalita, Jugal |
| description | Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2074371984</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2074371984</sourcerecordid><originalsourceid>FETCH-proquest_journals_20743719843</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSQcUlNLVDwSU0sysvMS1dILCjIyUxNUSjJV_DzCeBhYE1LzClO5YXS3AzKbq4hzh66BUX5haWpxSXxWfmlRXlAqXgjA3MTY3NDSwsTY-JUAQDZASrV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2074371984</pqid></control><display><type>article</type><title>Deep Learning applied to NLP</title><source>Free E- Journals</source><creator>Marc Moreno Lopez ; Kalita, Jugal</creator><creatorcontrib>Marc Moreno Lopez ; Kalita, Jugal</creatorcontrib><description>Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Artificial neural networks ; Computer vision ; Deep learning ; Image classification ; Microprocessors ; Natural language processing ; Neural networks ; Vision systems</subject><ispartof>arXiv.org, 2017-03</ispartof><rights>2017. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Marc Moreno Lopez</creatorcontrib><creatorcontrib>Kalita, Jugal</creatorcontrib><title>Deep Learning applied to NLP</title><title>arXiv.org</title><description>Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP.</description><subject>Artificial neural networks</subject><subject>Computer vision</subject><subject>Deep learning</subject><subject>Image classification</subject><subject>Microprocessors</subject><subject>Natural language processing</subject><subject>Neural networks</subject><subject>Vision systems</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSQcUlNLVDwSU0sysvMS1dILCjIyUxNUSjJV_DzCeBhYE1LzClO5YXS3AzKbq4hzh66BUX5haWpxSXxWfmlRXlAqXgjA3MTY3NDSwsTY-JUAQDZASrV</recordid><startdate>20170309</startdate><enddate>20170309</enddate><creator>Marc Moreno Lopez</creator><creator>Kalita, Jugal</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20170309</creationdate><title>Deep Learning applied to NLP</title><author>Marc Moreno Lopez ; Kalita, Jugal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_20743719843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Artificial neural networks</topic><topic>Computer vision</topic><topic>Deep learning</topic><topic>Image classification</topic><topic>Microprocessors</topic><topic>Natural language processing</topic><topic>Neural networks</topic><topic>Vision systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Marc Moreno Lopez</creatorcontrib><creatorcontrib>Kalita, Jugal</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Marc Moreno Lopez</au><au>Kalita, Jugal</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Deep Learning applied to NLP</atitle><jtitle>arXiv.org</jtitle><date>2017-03-09</date><risdate>2017</risdate><eissn>2331-8422</eissn><abstract>Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2017-03 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_2074371984 |
| source | Free E- Journals |
| subjects | Artificial neural networks Computer vision Deep learning Image classification Microprocessors Natural language processing Neural networks Vision systems |
| title | Deep Learning applied to NLP |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T07%3A19%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Deep%20Learning%20applied%20to%20NLP&rft.jtitle=arXiv.org&rft.au=Marc%20Moreno%20Lopez&rft.date=2017-03-09&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2074371984%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2074371984&rft_id=info:pmid/&rfr_iscdi=true |