Update results

README.md

@@ -116,12 +116,29 @@
     1. `(root_word, "ROOT")`
         - i.e. Head words for sentences
 
+## ***Part6. Ontology***
+**Files:**
+> **`6.1.BO_synsets_classifier.ipynb`**  
+- **Classification using Bag Of Synsets (BO)**
+> **`6.2.BOS_ParsingTree_NGrams.ipynb`**  
+- **Classification using Bag Of Synsets (BO) and other features from previous steps**
+![Ontology_results](./images/Ontology_results.png)
+
+
+## ***Part7. Word embedding***
+**Files:**
+> **`7.1.Word2Vec_classifier.ipynb`**  
+- **Classification using Word2Vec embedding weighted average for words vectors based on POS**
+> **`7.2.BERT_classifier.ipynb`**  
+- **Classification using BERT CLS token**
+![Embedding_result.png](./images/Embedding_result.png)
+
 ## ***Results***
 
 > ***Using augmented dataset*** 
 
 | Case\\Criterion                                              | Accuracy(Train) | Accuracy(Test) | Difference(%) | Precision(Test-Average) | Recall(Test-Average) | F1-Score(Test-Average) | Notes                          |
-| ------------------------------------------------------------ | --------------- | -------------- | ------------- | ----------------------- | -------------------- | ---------------------- | ------------------------- |
+| ------------------------------------------------------------ | --------------- | -------------- | ------------- | ----------------------- | -------------------- | ---------------------- | ------------------------------ |
 | nltk stemmer                                                 | 0.9852          | 0.9604         | 2.5           | 0.9593                  | 0.9587               | 0.9574                 | alpha=0.1, 450features         |
 | nltk lemmatizer                                              | 0.9891          | 0.9625         | 2.7           | 0.9635                  | 0.9626               | 0.9608                 | alpha=0.1, 700features         |
 | Stanza lemmatizer                                            | 0.9843          | 0.9646         | 2.0           | 0.9652                  | 0.9642               | 0.9623                 | alpha=0.1, 550features         |
@@ -141,7 +158,8 @@
 | BO synsets + POS filtering                                   | 0.9810          | 0.9271         | 5.4           | 0.9287                  | 0.9256               | 0.9224                 | alpha=0.01, 1500features       |
 | BO synsets + WSD                                             | 0.9961          | 0.9563         | 4.0           | 0.9594                  | 0.9564               | 0.9542                 | alpha=0.01,1750features        |
 | BO synsets + WSD + Stanza Dep. Relation tuples + (1,3) Grams | 0.9963          | 0.9708         | 2.5           | 0.9713                  | 0.9706               | 0.9683                 | alpha=0.01,5500features        |
-|                                                              |                 |                |               |                         |                      |                        |                           |
+| Word2Vec embedding using weighted vector based on POS        | 0.9632          | 0.9000         | 6.3           | 0.9079                  | 0.8970               | 0.8943                 | KNN, n=20, cosine, 185features |
+| BERT embedding using CLS token                               | 1.0000          | 0.9899         | 1.0           | 0.9861                  | 0.9886               | 0.9872                 | KNN, n=20, cosine, 695features |
 ---
 
 > ***Applied features selection and model's hyperparameters tuning***