Abstract
We explore the use of the well established lexical resource and theory of the Berkeley FrameNet project to support the creation of a domain-specific knowledge graph in the financial domain, more precisely from financial customer interactions. We introduce a domain independent and unsupervised method that can be used across multiple applications, and test our experiments on the financial domain. We use an existing tool for term extraction and taxonomy generation in combination with information taken from FrameNet. By using principles from frame semantic theory, we show that we can connect domain-specific terms with their semantic concepts (semantic frames) and their properties (frame elements) to enrich knowledge about these terms, in order to improve the customer experience in customer-agent dialogue settings.- Anthology ID:
- 2023.gwc-1.11
- Volume:
- Proceedings of the 12th Global Wordnet Conference
- Month:
- January
- Year:
- 2023
- Address:
- University of the Basque Country, Donostia - San Sebastian, Basque Country
- Editors:
- German Rigau, Francis Bond, Alexandre Rademaker
- Venue:
- GWC
- SIG:
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 91–100
- Language:
- URL:
- https://aclanthology.org/2023.gwc-1.11
- DOI:
- Cite (ACL):
- Cécile Robin, Atharva Kulkarni, and Paul Buitelaar. 2023. Identifying FrameNet Lexical Semantic Structures for Knowledge Graph Extraction from Financial Customer Interactions. In Proceedings of the 12th Global Wordnet Conference, pages 91–100, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.
- Cite (Informal):
- Identifying FrameNet Lexical Semantic Structures for Knowledge Graph Extraction from Financial Customer Interactions (Robin et al., GWC 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.gwc-1.11.pdf