Textual case-based reasoning (TCBR) is a subtopic of case-based reasoning, in short CBR, a popular area in artificial intelligence. CBR suggests the ways to use past experiences to solve future similar problems, requiring that past experiences be structured in a form similar to attribute-value pairs. This leads to the investigation of textual descriptions for knowledge exploration whose output will be, in turn, used to solve similar problems.
Subareas
Textual case-base reasoning research has focused on:
- measuring similarity between textual cases
- mapping texts into structured case representations
- adapting textual cases for reuse
- automatically generating representations.
References
- ^ Weber, R.O.; K., Ashley; S., Brüninghaus (2005). "Textual Case-Based Reasoning". Knowledge Engineering Review. 20 (3): 255–260. CiteSeerX 10.1.1.91.9022. doi:10.1017/S0269888906000713. S2CID 11502038.
External links
- Fourth Workshop on Textual Case-Based Reasoning: Beyond Retrieval
- Textual Case-Based Reasoning Wiki Archived 2012-06-15 at the Wayback Machine
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