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ECNLPIR 2027: Eurasian Conference on Natural Language Processing and Information Retrieval

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ECNLPIR
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截稿日期:
2027-04-09 还有 186 天
通知日期:
2027-06-24
会议日期:
2027-09-18
会议地点:
Barcelona, Spain
届数:
浏览: 20304   关注: 4   参加: 0

会伴指数 (CP-I)

42.1 / 100
全站第 4,158 名 / 共 5,693 个会议 · 前 74%

自然语言处理与语音 第 65 / 85 数据挖掘与数据库 第 254 / 337

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%)
19
社区关注 (10%)
26
资料公开度 (15%)
55

用到的输入: 有据可查的届次：1 · 在会伴关注它的研究者：4 人 · 过去 24 个月打开过本页的研究者：3 人

公开资料里还缺: 历年录用率 (+4.5) · 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 2026-10-04

征稿

ECNLPIR 2027 (Eurasian Conference on Natural Language Processing and Information Retrieval) is an academic conference held in Barcelona, Spain on 2027-09-18. The paper submission deadline is 2027-04-09. Acceptance notifications are sent on 2027-06-24.

We invite submissions on a wide range of research topics, spanning both theoretical and systems research. The topics of interest include, but are not limited to: Track 1: Natural Language Processing Technology Author identification and plagiarism detection Document summarisation and identification Natural language text generation from knowledge graphs and ontologies Sentiment analysis Opinion, personality and emotion detection in social media Methods for Classification and Categorisation Topic recognition and topic tracking, subject indexing Methods and systems of automatic machine translation Natural Language Processing for Digital Humanities Resources for basic Natural Language Processing tasks analysis Track 2: Information Retrieval, Mining & Data Analytics Adversarial and cross language information retrieval Aspects of high performance text analysis Collaborative information filtering Data mining、Data science、Text mining Event and anomaly detection Information, knowledge and taxonomy extraction Information mining and psycholinguistic Interactive, dynamic, contextual and personalised Information Retrieval Query Expansion Social and Multimedia Information Retrieval Text analytics for the Internet of Things Propagation and diffusion of information Super popular content Information Retrieval result evaluation and relevance feedback Track 3: Knowledge Graph, Semantic Web & Intelligent System Automated knowledge acquisition and representation Decentralised knowledge representation Graph and deep-learning-based methods of NLP & IR Knowledge graph learning, Knowledge representation, Knowledge discovery Semantic search and exploratory search Learning from Big Data for web-scale knowledge graphs Lexical semantics for the Semantic Web Intelligent Tutoring, Interactive Systems Methods and analyses for statistical networks Ontology generation, merging and verification methods Ontology learning、Ontology population Semantic similarity metrics and analogy Similarity analysis, clustering, hierarchical clustering Small world graphs Use of knowledge graphs and ontologies for NLP Visualisation of NLP and IR results
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