Abstract
This work introduces CONCEPTUM, an advanced knowledge discovery system for speed-reading natural language texts and allowing faster and more effective learning. CONCEPTUM sports a huge plethora of features, ranging from language detection and conceptualization, up to semantic categorization, named entity recognition and automatic ontology building, effectively turning an unstructured textual source into concepts, topics, relationships and summaries to quickly and easily browse it and classify it. The system does not require any training or configuration and at present can be applied as-is on general-purpose English and Italian texts, providing disparate kinds of users with a powerful means to significantly speed up and improve their learning and research activities. In this work, a challenging experimentation on the Biochemistry field is reported to highlight and discuss the arising critical issues in the application of the system on a highly-technical domain.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 357-361 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-5090-4124-4 |
| DOIs | |
| Publication status | Published - 2016 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
Keywords
- Conceptualization
- Information extraction
- Knowledge discovery
- Language detection
- Literature mining
- Named entity recognition
- Natural language processing
- Ontology building
- Semantic categorization
- Semantic graph
- Summarization
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