Update README.md

README.md

@@ -15,21 +15,29 @@ The current dataset contains five sampled splits, used in the supervised experim
 
 ## Data Structure
 
-The data are organised around the five `Train/test_split#`, each containing a training and test set of circa 60K and 2K.
+The `dataset` is organised around five `Train/test_split#`, each containing a training and test set of circa 60K and 2K.
 
 ### Features
 
 Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy`
 - `seq`:test sequense
-- `label`: ground truth (1:entail, 0:no-entailment)
+- `label`: ground truth (1:entialment, 0:no-entailment)
 - `Adj_Class`: the class of the sequence adjectives
-- `Adj`: the adjective of the sequence (I:intersective, S:subsective, O:intensional)
+- `Adj`: the adjective of the sequence (I: intersective, S: subsective, O: intensional)
 - `N`n: the noun
 - `Hy`: the noun's hypericum
 
+Each sample in `seq` can take one of three forms (or inference types, in paper):
+
+- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
+- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
+- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
+
+Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail. 
+
 ### Trained Model
 
-You can find a pre-trend BERT model (on the 2nd out-of-distribution split) [here](https://huggingface.co/lorenzoscottb/bert-base-cased-PLANE-ood-2?text=A+fake+smile+is+a+smile).
+You can find a tuned BERT-base model (tuned and validated using the 2nd split) [here](https://huggingface.co/lorenzoscottb/bert-base-cased-PLANE-ood-2?text=A+fake+smile+is+a+smile).
 
 ### Cite