26th International Conference on Natural Language & Information Systems June 23 - 25; DFKI Saarbrücken, Germany NLDB 2021 will be held as a video conference, or as a mixed-mode conference, if feasible Important Dates • Submission deadline: 08.03.2021 • Notification of acceptance: 8.04.2021 • Camera-ready due: 22.04.2021 • NLDB Conference: 23 - 25.06.2021 Invited Speakers Elke Teich, Saarland University, Germany Manfred Stede, Potsdam University, Germany A special session with the NL4XAI consortium (Interactive Natural Language Technology for Explainable Artificial Intelligence) has been planned Webpage: http://nldb2021.sb.dfki.de Contact: email@example.com Submission Guidelines Formatting: LNCS http://www.springer.de/comp/lncs/authors.html Submission website: https://easychair.org/my/conference?conf=nldb2021 Full papers: 12 pages maximum including references Short papers: 8 pages, including references Papers for a poster presentation: 6 pages, including references The programme committee may decide to accept some full papers as short papers or poster papers. Conference proceedings will be published by Springer LNCS. Previous proceedings are available at: https://link.springer.com/conference/nldb NLDB 2021 Since 1995, the NLDB conference brings together researchers, industry practitioners, and potential users interested in various application of Natural Language in the Database and Information Systems fields. The term “Information Systems” has to be considered in the broader sense of Information and Communication Systems, including Big Data, Linked Data and Social Networks. The field of Natural Language Processing (NLP) has itself recently experienced several exciting developments. In research, these developments have been reflected in the emergence of neural language models (e.g., Deep Learning, Word2Vec) and innovative neural network architectures (e.g., transformers). There is also a renewed interest in various linguistic phenomena, such as discourse and argumentation theory (argumentation mining) and the role of natural language in explaining complex systems. Regarding applications, NLP systems have evolved to the point that they now offer real-life, tangible benefits to enterprises. Many of these NLP systems are now considered a de-facto offering in business intelligence suites, such as algorithms for recommender systems and opinion mining/sentiment analysis as well as question-answering systems and conversational search and chatbots. It is against this backdrop of recent innovations in NLP and its applications in information systems that the 26th edition of the NLDB conference, NLDB2021, takes place. We welcome research and industrial contributions, describing novel, previously unpublished work on NLP and its applications across a plethora of topics. Indicative Topics Topics of interest include but are not limited to: • Argumentation Mining and Applications: Automatic detection of argumentation components and relationships, Creation of resources, e.g. annotated corpora, treebanks and parsers, Integration of NLP techniques with formal, Abstract argumentation structures, Argumentation Mining from legal texts and scientific articles. • Deep Learning, Neural Language models and NLP: Word2Vec applications, e.g. opinion mining, text summarization, machine translation, Development of novel deep learning architectures and algorithms, Parallel computation techniques and GPU programming for neural language models. • Social Media and Web Analytics: Plagia- rism detection, Opinion mining/sentiment analysis, detection of fake reviews, Information extraction: NER, Event detection, term and semantic relation- ship extraction, Text classification and clustering, Corpus analysis, Language detection, Robust NLP methods for sparse, ill formed texts, Recommendation systems. • 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 and multi-cultural corpus, Machine translation, Text analysis, Classification systems, Extraction, Named entity and event extraction. • Semantic Web, Open Linked Data, and Ontologies: Ontology learning and alignment, Ontology population, Ontology evaluation, Querying on- tologies and linked Data, Semantic tagging and clas- sification, Ontology driven NLP, Ontology driven systems integration. • Natural Language in Conceptual Modeling: 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, 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.