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MNLP 2020: IEEE Conference on Machine Learning and Natural Language Processing

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MNLP
Día de Entrega:
2020-07-19 Extended
Fecha de Notificación:
2020-09-21
Fecha de conferencia:
2020-12-12
Ubicación:
Agadir - Essaouira, Morocco
Ediciones:
4
Vistas: 13990   Seguidores: 3   Asistentes: 1

Solicitud de Artículos

MNLP 2020 (IEEE Conference on Machine Learning and Natural Language Processing) is an academic conference held in Agadir - Essaouira, Morocco on 2020-12-12. The paper submission deadline is 2020-07-19 (extended). Acceptance notifications are sent on 2020-09-21.

The 4th IEEE Conference on "Machine Learning and Natural Language Processing: Models, Systems, Data and Applications" will be held within IEEE CiSt'20, the week of December 12th – 18th 2020, Agadir - Essaouira, Morocco. The MNLP conference aims to explore and debate the latest technical status and recent innovations trends in the research and applications of natural language processing and machine learning models and technologies. The purpose of the conference is to provide an opportunity for the leading academic scientists, researchers, engineers, industrialists, scholars and other professionals from all over the world to interact and exchange their new ideas and research outcomes in related fields and develop possible chances for future collaboration. The conference is also aimed at motivating the next generation of researchers to promote their interests in machine learning and NLP. NLP technologies and digital resources such as lexical databases, ontologies and corpora are instrumental to millions of people who use them every day without even being aware of them. Systems like Google Translate and web search engines rely more and more on levels of linguistic information automatically provided through NLP tools, and on huge repositories of structured language data. This development has deeply affected the way we think about language from a scientific perspective. Linguists have progressively turned their focus from abstract properties of language to the dynamics of its usage in communicative contexts. There is increasing awareness that language digital repositories, technologies and computational models of language processes not only shed considerable light on traditional issues in language and literary studies but may also lead to a radical reconceptualization of them. Aspects of language acquisition, lexical access, speech recognition, text translation, text analysis, ontology extraction, reading and optical character recognition can be put to a rigorous empirical test through computer simulations. Likewise, corpora, lexical databases and ontologies have helped us focus on the nature of language data from a different perspective. From this standpoint, digital data and computer technologies provide an empirical middle ground where interdisciplinary insights can be empirically assessed and integrated. This conference is therefore equally intended to explore and debate the contribution of computational language models and language data to a better understanding of linguistic, psycholinguistic, sociolinguistic, historical and literary issues of the language and culture. We particularly welcome contributions addressing fundamental aspects of NLP, communication technology, language resources and automated text analysis by looking at their implications for both state-of-the-art language technologies and language knowledge at large. TOPICS We solicit submissions of original ideas and papers describing significant results and developments from both researchers and practitioners in a range of fields related but not limited to any of the following topics : Information retrieval and extraction Data, Web, and Text mining Named entity recognition Question answering Word sense disambiguation Machine Translation Stochastic language models Connectionist language models Language technologies for cultural heritage Optical character recognition Digital epigraphy Machine Learning models and applications Artificial Intelligence and Recommender systems Neural Networks Deep Learning Pattern Recognition Computer Vision
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