# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 6 Conclusions  
We focused our work on the design and verification of procedures for the field of natural language processing. Within this area, we chose the issue of coreference identification and a dataset containing fake news. We used the coreference algorithm for the input data, and, thus, we created two sets of texts--a text with the application of coreference resolution and a text without the application of coreference resolution. Subsequently, we evaluated these two texts using vector models, namely TF-IDF and Doc2 Vec. Finally, we implemented various machine-learning-based classifiers in our research, from which we selected the Decision Tree, Random Forest, K-Nearest Neighbors, MultinomialNB, and Logistic Regression methods as the most suitable. The classifiers categorized news articles as either fake or real news based on their classification result. According to the achieved results, the data processing with the application of coreference resolution resulted in an increase in the accuracy of the classification of fake news. In the case of the accuracy performance measure, the best result was observed for the Random Forest classifier using the _Tfdf_coef_ method (_median_ = 0.8149). For the F1 score, the best result was observed for the MultinomialNB classifier using the _Tfdf_coef_ method (_median_ = 0.8101). When looking at the results in terms of increased accuracy, an improvement was observed in all methods except for the Decision Tree classifier with Doc2 Vec, where a decrease in accuracy was observed when comparing the metrics _coef_ and _without_. The most significant increase in accuracy was observed for the K-Nearest Neighbors classifier with the TF-IDF embedding method (0.0942 increase for _coref_). Regarding the results from the perspective of an increased F1 score, an improvement was observed in all the methods except for the Decision Tree and MultinomialNB classifiers with Doc2 Vec, where a decrease in accuracy was observed when comparing the metrics _coef_ and _without_. The most notable increase in the F1 score was observed for the K-Nearest Neighbors classifier and the TF-IDF embedding method (0.1557 increase for _coref_). Coreference resolution represents a single method for dataset preparation in the classification tasks of fake news identification. For this reason, even a slight improvement in the method could cause an important shift in the researched area, because the method would be used as part of other methods for dataset preparation. We want to find and use other tools for the application of coreference resolution in future work. The neuralcoref library was used as a deep learning neural network model in this study. This library has not been updated for a long time, and its implementation is only possible with the support of the Space 2.1.0 software, which has long been replaced by newer versions. It is clear from the results that the method needs to be verified with several other datasets. We plan to validate this method on other datasets with a different thematic focus. An important part of further work would also be improving the accuracy of the method in various other classification tasks of natural language processing, such as classifications of text type, content, or tone. It would also be a research challenge to validate this method in collaboration with other word vector preparation methods. Conceptualization, K.S. and J.K.; methodology, K.S. and J.K.; validation, K.S. and J.K.; formal analysis, K.S. and J.K.; resources, K.S. and J.K.; data curation, K.S. and J.K.; writing--original draft preparation, K.S. and J.K.; writing--review and editing, K.S. and J.K.; visualization, K.S. ; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and Slovak Academy of Sciences under Contract VEGA-1/0490/22, also by the Slovak Research and Development Agency under contract no.