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Survey on Advancements in Machine Learning for Natural Language Processing
This paper explores significant advances in machine learning (ML) in the field of natural language processing (NLP), with an emphasis on transformative innovations such as transformer models and large language models (LLMs). By facilitating the ability of machines to understand and generate human la...
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| creator | Hamed, Osama Amer, Mohammed Bejaoui, Tarek |
| description | This paper explores significant advances in machine learning (ML) in the field of natural language processing (NLP), with an emphasis on transformative innovations such as transformer models and large language models (LLMs). By facilitating the ability of machines to understand and generate human language, these technologies have revolutionized applications such as automated translation and conversational agents. In addition, the paper addresses ethical considerations, including data bias and model interpretability, that pose challenges to the practical use of ML-driven NLP systems. The discussion highlights both breakthroughs and obstacles in this evolving field, and provides insights into future research directions aimed at refining the capabilities and responsible use of NLP technologies. |
| doi_str_mv | 10.1109/ISNCC62547.2024.10759035 |
| format | conference_proceeding |
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The discussion highlights both breakthroughs and obstacles in this evolving field, and provides insights into future research directions aimed at refining the capabilities and responsible use of NLP technologies.</description><identifier>EISSN: 2768-0940</identifier><identifier>EISBN: 9798350364910</identifier><identifier>DOI: 10.1109/ISNCC62547.2024.10759035</identifier><language>eng</language><publisher>IEEE</publisher><subject>Ethics ; Large Language Models ; Machine Learning ; Natural language processing ; Optimization ; Privacy ; Refining ; Reinforcement learning ; Surveys ; Technological innovation ; Trajectory ; Transfer Learning ; Transformers</subject><ispartof>International Symposium on Networks, Computers and Communications, 2024, p.1-6</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10759035$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27904,54533,54910</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10759035$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hamed, Osama</creatorcontrib><creatorcontrib>Amer, Mohammed</creatorcontrib><creatorcontrib>Bejaoui, Tarek</creatorcontrib><title>Survey on Advancements in Machine Learning for Natural Language Processing</title><title>International Symposium on Networks, Computers and Communications</title><addtitle>ISNCC</addtitle><description>This paper explores significant advances in machine learning (ML) in the field of natural language processing (NLP), with an emphasis on transformative innovations such as transformer models and large language models (LLMs). 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The discussion highlights both breakthroughs and obstacles in this evolving field, and provides insights into future research directions aimed at refining the capabilities and responsible use of NLP technologies.</description><subject>Ethics</subject><subject>Large Language Models</subject><subject>Machine Learning</subject><subject>Natural language processing</subject><subject>Optimization</subject><subject>Privacy</subject><subject>Refining</subject><subject>Reinforcement learning</subject><subject>Surveys</subject><subject>Technological innovation</subject><subject>Trajectory</subject><subject>Transfer Learning</subject><subject>Transformers</subject><issn>2768-0940</issn><isbn>9798350364910</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNqFzsGKwjAQgOG4IChr38DDvIB1krSNOUpRdHFlQe8y1LFGNJXECr69HvS8p__wXX4hQGIqJdrxcrMuy0LlmUkVqiyVaHKLOu-IxBo70TnqIrMSv0RfmWIyQpthTyQxnhBRS2OMVH3xs2nDnR_QeJju7-QrvrC_RXAefqk6Os-wYgre-RoOTYA13dpAZ1iRr1uqGf5CU3GMLx-I7oHOkZN3v8VwPtuWi5Fj5t01uAuFx-6zqf_hJyzXQLw</recordid><startdate>20241022</startdate><enddate>20241022</enddate><creator>Hamed, Osama</creator><creator>Amer, Mohammed</creator><creator>Bejaoui, Tarek</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20241022</creationdate><title>Survey on Advancements in Machine Learning for Natural Language Processing</title><author>Hamed, Osama ; Amer, Mohammed ; Bejaoui, Tarek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_107590353</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Ethics</topic><topic>Large Language Models</topic><topic>Machine Learning</topic><topic>Natural language processing</topic><topic>Optimization</topic><topic>Privacy</topic><topic>Refining</topic><topic>Reinforcement learning</topic><topic>Surveys</topic><topic>Technological innovation</topic><topic>Trajectory</topic><topic>Transfer Learning</topic><topic>Transformers</topic><toplevel>online_resources</toplevel><creatorcontrib>Hamed, Osama</creatorcontrib><creatorcontrib>Amer, Mohammed</creatorcontrib><creatorcontrib>Bejaoui, Tarek</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hamed, Osama</au><au>Amer, Mohammed</au><au>Bejaoui, Tarek</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Survey on Advancements in Machine Learning for Natural Language Processing</atitle><btitle>International Symposium on Networks, Computers and Communications</btitle><stitle>ISNCC</stitle><date>2024-10-22</date><risdate>2024</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><eissn>2768-0940</eissn><eisbn>9798350364910</eisbn><abstract>This paper explores significant advances in machine learning (ML) in the field of natural language processing (NLP), with an emphasis on transformative innovations such as transformer models and large language models (LLMs). By facilitating the ability of machines to understand and generate human language, these technologies have revolutionized applications such as automated translation and conversational agents. In addition, the paper addresses ethical considerations, including data bias and model interpretability, that pose challenges to the practical use of ML-driven NLP systems. The discussion highlights both breakthroughs and obstacles in this evolving field, and provides insights into future research directions aimed at refining the capabilities and responsible use of NLP technologies.</abstract><pub>IEEE</pub><doi>10.1109/ISNCC62547.2024.10759035</doi></addata></record> |
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| identifier | EISSN: 2768-0940 |
| ispartof | International Symposium on Networks, Computers and Communications, 2024, p.1-6 |
| issn | 2768-0940 |
| language | eng |
| recordid | cdi_ieee_primary_10759035 |
| source | IEEE Xplore All Conference Series |
| subjects | Ethics Large Language Models Machine Learning Natural language processing Optimization Privacy Refining Reinforcement learning Surveys Technological innovation Trajectory Transfer Learning Transformers |
| title | Survey on Advancements in Machine Learning for Natural Language Processing |
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