'''Textual case-based reasoning''' (TCBR) is a subtopic of [[Casecase-based reasoning]], in short CBR, a popular area in [[Artificialartificial Intelligenceintelligence]]. Basically CBR suggests the ways to use past experiences to solve future similar problems.. However, it requires a prerequisiterequiring that past experiences should be structured in a form similar to [[attribute-value pairs. In recent days{{when?|date=December 2011}}, users share their vast experiences through blogs and popular messaging services like [[twitter]]. In such textual descriptions, how to find and extract the knowledge relations in the form 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.<ref name=":0">{{Cite journal|last1=Weber|first1=R.O.|last2=K.|first2=Ashley|last3=S.|first3=Brüninghaus|date=2005|title=Textual Case-Based Reasoning|journal=Knowledge Engineering Review|volume=20|issue=3 |pages=255–260|doi=10.1017/S0269888906000713 |citeseerx=10.1.1.91.9022 |s2cid=11502038 }}</ref>
== Subareas ==
Textual case-base reasoning research has focused on:
* measuring similarity between textual cases<ref name=":0" />
* mapping texts into structured case representations<ref name=":0" />
* adapting textual cases for reuse<ref name=":0" />
