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

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截稿日期:
2024-03-22
通知日期:
2024-04-19
会议日期:
2024-06-25
会议地点:
Turin, Italy
届数:
29
浏览: 9143   关注: 0   参加: 0

会伴指数 (CP-I)

50.4 / 100
全站第 1,506 名 / 共 5,684 个会议 · 前 27%

自然语言处理与语音 第 26 / 85 信息系统与 Web 第 75 / 253

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
92
社区关注 (10%)
8
资料公开度 (15%)
25

用到的输入: 有据可查的届次：29 · 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-24

征稿

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NLDB 2024 invites authors to submit papers for oral or poster presentations on unpublished research that addresses theoretical aspects, algorithms, applications, architectures for applied and integrated NLP, resources for applied NLP, and other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.
由 Dou Sun 最后更新于

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