# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 4 Results  
We applied the existing coreference resolution method to the selected dataset using the neuralcoref library. We cleaned the data and removed null values in the form of empty rows in the pre-processing stage of our work. We did not remove the stop words or lemmatization since we could have significantly disrupted the identification of pronouns, as the library needs to work with the original text. We used the Scikit-learn library to create word vectors using TF-IDF. The gensim library was used for the Doc2 Vec method. The investigated classification models were created using the methods of the Scikit-learn library. We used K-fold validation for all the models, and we set the number of splits as \(k=10\). Five classification methods (Decision Tree, Random Forest, K-Nearest Neighbors, MultinomialNB, and Logistic Regression) and two word-embedding methods (TF-IDF, Doc2 Vec) were selected. The used methods were examined using K-fold cross-validation, where k represented 10 measurements. In this way, 160 classification models were created(data without coreferencing and data with coreferencing (2) \(\times\) word embedding methods (2) \(\times\) classification methods (4) \(\times\) k-fold validation (10)) for the investigated fake news dataset. The quality of the created models was evaluated using evaluation metrics (_accuracy_, _precision_, _recall_, _f1 score_, _precision_fake_, _recall_fake_, _precision_real_, and _recall_real_). The individual stages of our proposed method are described in Figure 2.  
The descriptive statistics for the accuracy results are presented in Table 3. The Valid N value represents the four classification methods used for the 10-fold cross-validation. The mean and median proved that better results were observed for the TF-IDF method compared to the Doc2 Vec method. After closer inspection, better outcomes were achieved (mean and median) for the models created from the input data after applying coreference resolution. The range of the variation in both word-embedding methods (_Tfdf_coref_ and _Doc2 Vec_coref_) also decreased. Similarly, better results were also observed in the case of coreference resolution for the other performance measures. The results for the F1 score, which is a combination of precision and recall, are presented in Table 4.  
The results of the accuracy and F1 score (Tables 3 and 4) showed that the difference between the models created from the datasets without and with the application of coreference resolution were not so striking. Similarly, insufficient results of the models were observed with the other evaluation measurements (_precision_, _recall_, _precision_fake_, _recall_fake_, _precision_real_, and _recall_real_). Therefore, we analyzed the results for the used classification algorithms individually in more detail. The results of the 10-fold cross-validation for each classification technique (Decision Tree, Random Forest, K-Nearest Neighbors, MultinomialNB, and Logistic Regression) depended on the methods of preparing the input vectors for the classification methods. The input vectors were prepared without coreference resolution (parameter_without) and with coreference resolution (parameter_coref). At the same time, two word-embedding methods (TF-IDF and Doc2 Vec) were applied for the preparation of the vectors. The results for accuracy are visualized in the following individual graphs (Figure 7a-d). We compared the combinations of the text preparation methods (_without_ and _coref) and word-embedding methods (TF-IDF and Doc2 Vec).