Upload README.md

README.md

@@ -55,25 +55,25 @@ Twitter data. Through fine-tuning with the [TACO](https://doi.org/10.5281/zenodo
 Relevant Argument Properties (WRAP) into the embedding space.
 
 ## Class Semantics
-
-WRAPresentations, to some degree, captures the semantics of the critical components of an argument (inference and information), as defined by the 
-[Cambridge Dictionary](https://dictionary.cambridge.org).
+The TACO framework revolves around the two key elements of an argument, as defined by the [Cambridge Dictionary](https://dictionary.cambridge.org).
+WRAPresentations, to some degree, captures the semantics of these critical components in its embedding space.
 It encodes *inference* as *a guess that you make or an opinion that you form based on the information that you have*, and it also leverages the 
 definition of *information* as *facts or details about a person, company, product, etc.*.
 
-Consequently, it has also learned the semantics of:
+Consequently, it has also learned the class semantics, where inferences and information can be aggregated in relation to these distinct
+classes containing these components:
 
-* *Statement*, which refers to unique cases where only the *inference* is presented as *something that someone says or writes officially, or an
-action done to express an opinion*.
-* *Reason*, which represents a full argument where the *inference* is based on direct *information* mentioned in the tweet, such as a
-source-reference or quotation, and thus reveals the author’s motivation *to try to understand and to make judgments based on practical facts*.
+* *Statement*, which refers to unique cases where only the *inference* is presented as *something that someone says or writes officially, or an action
+done to express an opinion*.
+* *Reason*, which represents a full argument where the *inference* is based on direct *information* mentioned in the tweet, such as a source-reference
+or quotation, and thus reveals the author’s motivation *to try to understand and to make judgments based on practical facts*.
 * *Notification*, which refers to a tweet that limits itself to providing *information*, such as media channels promoting their latest articles.
 * *None*, a tweet that provides neither *inference* nor *information*.
 
 In its entirety, WRAPresentations encodes the following hierarchy for tweets:
 
 <div align="center">
-  <img src="https://github.com/TomatenMarc/public-images/raw/main/Argument_Tree.svg" alt="Argument Tree" width="100%">
+  <img src="https://github.com/TomatenMarc/public-images/raw/main/Component_Space_WRAP.svg" alt="Component Space" width="100%">
 </div>
 
 ## Class Semantic Transfer to Embeddings