Información de la conferencia

NLDB' 2024: International Conference on Natural Language & Information Systems

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Día de Entrega:
2024-03-22
Fecha de Notificación:
2024-04-19
Fecha de conferencia:
2024-06-25
Ubicación:
Turin, Italy
Ediciones:
29
Vistas: 9141   Seguidores: 0   Asistentes: 0

Índice Conference Partner (CP-I)

50,4 / 100
Puesto n.º 1.506 de 5.684 congresos · 27% superior

N.º 26 de 85 en Procesamiento del lenguaje natural y voz N.º 75 de 253 en Sistemas de información y Web

Reconocimiento académico (35%) Sin datos: se puntúa con la línea base neutra de 50 —
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
92
Atención de la comunidad (10%)
8
Integridad del registro público (15%)
25

Datos utilizados: Ediciones documentadas: 29 · Investigadores que abrieron esta página en los últimos 24 meses: 2

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Confianza 45 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-24

Solicitud de Artículos

NLDB' 2024 (International Conference on Natural Language & Information Systems) is an academic conference held in Turin, Italy on 2024-06-25. The paper submission deadline is 2024-03-22. Acceptance notifications are sent on 2024-04-19.

NLDB 2024 invites authors to submit papers for oral or poster presentations on unpublished research that addresses theoretical aspects, algorithms, applications, architectures for applied and integrated NLP, resources for applied NLP, and other aspects of NLP, as well as survey and discussion papers. This year’s edition of NLDB continues with the Industry Track, to foster fruitful interaction between the industry and the research community. Topics of interest include but are not limited to: Large Language Models: training, applications, transfer learning, interpretability of large language models. Multimodal Models: Integration of text with other modalities like images, video, and audio; multimodal representation learning; applications of multimodal models. AI Safety and ethics: Safe and ethical use of Generative AI and NLP; avoiding and mitigating biases in NLP models and systems; explainability and transparency in AI. Natural Language Interfaces and Interaction: design and implementation of Natural Language Interfaces, user studies with human participants on Conversational User Interfaces, chatbots and LLM-based chatbots and their interaction with users. Social Media and Web Analytics: Opinion mining/sentiment analysis, irony/sarcasm detection; detection of fake reviews and deceptive language; detection of harmful information: fake news and hate speech; sexism and misogyny; detection of mental health disorders; identification of stereotypes and social biases; robust NLP methods for sparse, ill-formed texts; recommendation systems. Deep Learning and eXplainable Artificial Intelligence (XAI): Deep learning architectures, word embeddings, transparency, interpretability, fairness, debiasing, ethics. Argumentation Mining and Applications: Automatic detection of argumentation components and relationships; creation of resource (e.g. annotated corpora, treebanks and parsers); Integration of NLP techniques with formal, abstract argumentation structures; Argumentation Mining from legal texts and scientific articles. Question Answering (QA): Natural language interfaces to databases, QA using web data, multi-lingual QA, non-factoid QA(how/why/opinion questions, lists), geographical QA, QA corpora and training sets, QA over linked data (QALD). Corpus Analysis: multi-lingual, multi-cultural and multi-modal corpora; machine translation, text analysis, text classification and clustering; language identification; plagiarism detection; information extraction: named entity, extraction of events, terms and semantic relationships. Semantic Web, Open Linked Data, and Ontologies: Ontology learning and alignment, ontology population, ontology evaluation, querying ontologies and linked data, semantic tagging and classification, ontology-driven NLP, ontology-driven systems integration. Natural Language in Conceptual Modelling: Analysis of natural language descriptions, NLP in requirement engineering, terminological ontologies, consistency checking, metadata creation and harvesting. Natural Language and Ubiquitous Computing: Pervasive computing, embedded, robotic and mobile applications; conversational agents; NLP techniques for Internet of Things (IoT); NLP techniques for ambient intelligence Big Data and Business Intelligence: Identity detection, semantic data cleaning, summarisation, reporting, and data to text. This year’s conference tracks are: The main track solicits novel and significant research contributions addressing theoretical aspects, algorithms, applications, architectures, resources, and other aspects of NLP, as well as survey and discussion papers. We welcome work describing original and replicable research showing evidence of significant contribution to the NLP community. The industry track covers all aspects of innovative commercial or industrial-strength NLP technologies in order to showcase the state of adoption. It welcomes contributions about case studies of success stories, discussion reports of obstacles that stand in the way of adoption of NLP technologies, and experience reports in applying recent research advances to relevant industry problems. We encourage results and ideas from companies small and large.
Última actualización por Dou Sun el

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