# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 5 Discussion  
We aimed at improving text classification using coreference resolution in the presented work. We used the freely available KaiDMML dataset, which contains 405 news articles, half of which were annotated as fake news and the other half as real news. The advantage of this dataset is its manual annotation. However, it is natural that the observed performance metrics of our models were lower using this dataset than in the case of the datasets that are not manually annotated. The second disadvantage of this dataset is its small size. We applied CR analysis to the selected dataset and compared the results of the classification of fake news without applying the CR method. The TF-IDF and Doc2 Vec methods were used to create the input vectors. We chose Decision Tree, Random Forest, K-Nearest Neighbors, MultinomialNB, and Logistic Regression from the classification methods. Despite our efforts, no significant differences in the results were identified. Therefore, we only analyzed the results with descriptive statistics, and we did not verify the statistical significance of the differences between the investigated approaches. This finding is important. The results were improved by applying the CR method. Unfortunately, its impact was not statistically significant. The only difference was observed for the K-Nearest Neighbors classifier. However, this classifier achieved very poor results compared to the other classifiers used. The difference in the results between the methods with CR and those without applying CR was therefore rather due to the poor results of the K-Nearest Neighbors classifier. The finding that CR led to better classification results but not to statistically significant differences was surprising. In the case of the TF-IDF method in particular, we expected significantly better results for CR. This expectation was given by the TF-IDF method itself, where the values of the vector element for specific objects were practically increased, e.g., nouns and decreased values and pronouns or abbreviations (mentions). We assumed that by increasing the values of the elements expressing specific objects at the expense of mentions, we would achieve significantly better results. However, it was clear from the results of the descriptive statistics that applying CR contributed to better results, especially when examining the median value for accuracy, the F1 score, and the precision_fake metric. For this reason, we can conclude that the application of CR improved the classification tasks in general. An interesting and surprising finding was that the TF-IDF method achieved better results compared to the Doc2 Vec method. In our opinion, this was caused by every creation of a vector in the Doc2 Vec method, which needs a robust corpus for its effective deployment. In our case, it was only 405 records. Although the results were not statistically significant, it can be concluded from the descriptive statistics that the application of CR can contribute to improving the classification of fake news. The application of coreference resolution (CR) to a dataset can be classified as a data augmentation technique. There are two related research studies in this area. [PERSON] et al. [18] used a text data augmentation technique--sentence permutations--to create synthetic data based on an existing labeled dataset. Their method achieved a significant improvement in terms of classification accuracy, averaging around 4.1% across eight different datasets. Additionally, they proposed two other methods for text data expansion: antonym replacement and negation. These methods were tested on three suitable datasets and achieved accuracy improvements of 0.35% (antonyms) and 0.4% (negation) compared to the permutation method they proposed. [PERSON] et al. [17] utilized four techniques for expanding the text corpus called easy data augmentation (EDA) which included synonym replacement, random insertion, swapping, and deletion.