Semantic Aspects in Natural Language Processing
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 2895
Special Issue Editor
Interests: natural language processing; semantic parsing; multimodal analysis; graph-based knowledge representation; formal languages; graph grammars; tree automata
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Natural language is the prevailing medium through which information is made available on the Internet: as text or speech, standing on its own, being accompanied by pictures or embedded in video, and coming in various forms from short, highly colloquial and often ungrammatical forum posts and tweets over commercials and scientific articles to legal texts. To make sense of this information, aggregate, search, evaluate or verify it, or present it to the user in a way that provides added value, software is needed that can make decisions based on the semantics or meaning of natural language utterances.
The aim of his Special Issue is to gather contributions—both original work and survey articles—regarding algorithms and language processing techniques for the semantic analysis of natural language, the representation of the results of such an analysis, and the further processing of these representations. Areas of interest include but are not limited to:
- Semantic parsing and meaning representation;
- Machine learning approaches to language understanding;
- Semantic analysis of natural language in a multimodal context;
- Language grounding and natural language interaction with, e.g., robots;
- Question answering;
- Sentiment analysis and emotion recognition;
- Identification of bias and fake news;
- Formal models and algorithms for any of the above.
Prof. Dr. Frank Drewes
Guest Editor
Manuscript Submission Information
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Keywords
- natural language processing
- semantic parsing
- meaning representation
- language understanding
- language grounding
- question answering
- sentiment analysis
- emotion identification
- bias
- fake news
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