# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 1 Introduction  
As a part of computational linguistics, coreference resolution is still a research challenge as it is not enough to only find the first occurrence of an entity in the overall analysis of a text; the correct identification and assignment of all verbal references to these entities are also necessary. In this work, we decided to combine the issue of coreference resolution with the area of identifying fake news. We focused on entity determination methods and compared whether substituting an entity for the original reference can improve and specify the text representation for classifiers. Natural language processing requires pre-processing the text into a form that is usable for the analysis and prediction of specific tasks. Coreference resolution (CR) involves searching and replacing the name of the given entity in the text. Every written text contains mentions or references to many entities from the real world, which may not always be labeled with the same words. Very often pronouns or synonyms of nominal phrases are used to avoid the repetition of words in the text. The mentioned method of referencing brings one problem: for each reference, its target entity must also be determined. CR deals with this problem. A coreference occurs when the same referent is referred to in the text under different terms. As an example, we can mention the person \"[PERSON]\", who can be mentioned later in a text as \"[PERSON]\", by the pronoun \"she\", or even by her initials \"[PERSON]\". Mentions can occur in the form of a noun phrase, a pronoun, a proper name, etc. Each of these phrasesrefers to the same person, and the main task of coreferencing is to identify him or her and to find out the relationship between a given group of expressions. Another problem is determining the gender of the referent because it makes it much easier to determine the relationships between individual entities. We can see the illustration of CR in Figure 1, where mentions are identified in blue, which will be replaced by specific entities. In our article, we focus on coreference resolution, which searches and identifies coreferential relationships between entities and their referring terms in a text. Using coreference resolution, we can add additional information to the text, e.g., replace pronouns with a weak declarative value by the objects they represent. This approach can be used to refine the representation of the text. The aim of this article is to find out whether pre-processing a text using coreference resolution can improve performance measures in classification tasks. One of the classification tasks is the classification of fake news, which was chosen from our previous work as we have experience with this type of task and, at the same time, we have a validated existing dataset. It is obvious that coreference resolution can be applied in solving other text data classification tasks as well. The basic goal of our work is to determine the significance of CR identification for identifying fake news messages. We believe that a text after CR identification will result in better outcomes. Our aim is not only to determine if the results will improve but also to quantify the improvement. Additionally, we aim to assess the impact of CR on the results based on two different embedding methods for creating feature vectors. We used CR as a preliminary preparation method for texts that will be used for the vectorization of documents. Vectors created from documents are often used as input vectors to create training and test sets for classifiers in natural language processing classification tasks. The following methodology (Figure 2) was used for the evaluation of the contribution of CR to improving classification tasks:  
1. Data preparation;
2. The identification of the coreference resolution in the data file;
3. The creation of word vectors and document vectors using the Doc2 Vec methods and the traditional TF-IDF method. The following datasets containing four-word vectors were created: 1.