Textual case-based reasoning
Today, Textual case-based reasoning is a topic of great relevance and interest to a wide spectrum of society. Whether it is a political debate, a cultural phenomenon, a technological advance or a historical event, Textual case-based reasoning arouses the curiosity and interest of millions of people around the world. In this article, we will explore in depth all aspects related to Textual case-based reasoning, analyzing its impact in different areas and offering a broad and complete vision of this topic that is so relevant today.
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
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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