Call for Papers
For full details, please have a look at the
Call for Papers.
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, Applications, e.g. in opinion mining/sentiment analysis
- Deep Learning, Neural Languages 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: Plagiarism detection, Opinion mining/sentiment analysis, detection of fake reviews, Information extraction: NER, event detection, term and semantic relationship extraction, Text summarization, 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 ontologies and Linked Data, Semantic tagging and classification, 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.
