# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 2 Related Work
In their research work, 17 well-defined syntactic and semantic features including the 13 baseline features with semantic role labeling (SRL) were used. The use of an SVM classifier led to a better outcome when compared to other machine learning models. [PERSON] [15] introduced the Treex CR system, which implemented a sequence of evaluation models for individual types of coreference expressions (personal, reflexive, indefinite, interrogative, and possessive pronouns). It used a rich set of features extracted from linguistically pre-processed data. The system seemed to outperform the baseline system in Czech. In English, although it could not outperform the best approaches in the Stanford system, its performance was high enough to be used in future experiments. [PERSON] et al. [16] proposed a solution to the problem of ambiguous pronouns in the English language. In their work, they used a dataset called GAP ([PERSON] et al., 2018), which contains 8908 labeled pairs of antecedent naming of a person and an ambiguous pronoun from the Wikipedia database. They trained the model using the BERT technique to obtain contextual embeddings from the text, which they applied to the SVM classifier. Their proposed model demonstrated promising performance; it had an accuracy of 78.35%, a precision of 75.82%, a recall of 69.30%, an F1 score of 71.50%, and a low loss of 0.53. In this article, we focus on improving the classification of textual data using coreference resolution. This approach is very similar to data augmentation methods. [PERSON] et al. [17] utilized four techniques for expanding the text corpus, synonym replacement, random insertion, swapping, and deletion, which are collectively known as easy data augmentation (EDA). Through experiments on five classification tasks, they found an improved performance for convolutional and recurrent neural networks. Models trained using EDA surpassed this number by achieving an average accuracy of 88.6% while only using 50%of the available training data. A limitation of this paper is that the performance gains achieved with the proposed data augmentation method (EDA) may be marginal when the dataset is already sufficient. While EDA shows promise for small datasets, it might not yield significant improvements when using pre-trained models like ULMFit, ELMo, or BERT. Additionally, comparing EDA with other data augmentation methods used in NLP becomes challenging due to variations in the models and datasets used in different studies. [PERSON] et al. [18] used a text data augmentation technique--sentence permutations--to create synthetic data based on an existing labeled dataset. As a conclusion from the mentioned research works, the solution to the CR issue entails the design of new methods and a search for ways in which we can identify references in the text in the most efficient way. Their permutation augmentation improved the baseline classification accuracy by 4% on average and outperformed all the other augmentations proposed in their work by an average of 0.2%. All the research works are summarized in Table 1.  
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