Deriving semantic knowledge from descriptive texts using an MT system
Eric Nyberg, Teruko Mitamura, Kathryn Baker, David Svoboda, Brian Peterson, Jennifer Williams
Abstract
This paper describes the results of a feasibility study which focused on deriving semantic networks from descriptive texts using controlled language. The KANT system [3,6] was used to analyze input paragraphs, producing sentence-level interlingua representations. The interlinguas were merged to construct a paragraph-level representation, which was used to create a semantic network in Conceptual Graph (CG) [1] format. The interlinguas are also translated (using the KANTOO generator) into OWL statements for entry into the Ontology Works electrical power factbase [9]. The system was extended to allow simple querying in natural language.- Anthology ID:
- 2002.amta-papers.15
- Volume:
- Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- October 8-12
- Year:
- 2002
- Address:
- Tiburon, USA
- Editor:
- Stephen D. Richardson
- Venue:
- AMTA
- SIG:
- Publisher:
- Springer
- Note:
- Pages:
- 145–154
- Language:
- URL:
- https://link.springer.com/chapter/10.1007/3-540-45820-4_15
- DOI:
- Cite (ACL):
- Eric Nyberg, Teruko Mitamura, Kathryn Baker, David Svoboda, Brian Peterson, and Jennifer Williams. 2002. Deriving semantic knowledge from descriptive texts using an MT system. In Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 145–154, Tiburon, USA. Springer.
- Cite (Informal):
- Deriving semantic knowledge from descriptive texts using an MT system (Nyberg et al., AMTA 2002)
- PDF:
- https://link.springer.com/chapter/10.1007/3-540-45820-4_15