# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 2 Related Work  
Coreference resolution is a method that usually supports other NLP techniques such as text summarization [1], a question answering system [2], or information extraction [3; 4]. It can be used in solving several classification tasks such as identifying and classifying fake news or detecting phishing messages [5]. [PERSON] et al. [6] presented the FAKTA framework, which integrates various components of a fact checking process, i.e., document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. [PERSON] et al. [7] described a rather simple pair-wise classification model for coreference resolution, which was developed with a well-designed set of features. Their work produced a state-of-the-art system that outperformed systems built with complex models. [PERSON] [8] investigated the improvement of Chinese CR. Their model proposes acquired word and character representations through pre-trained Skip-gram embeddings and pre-trained BERT. Then, it explicitly leverages span-level information by performing bidirectional LSTMs among the above representations. The proposed model achieved a 62.95% F1 score, outperforming their baseline methods. The only limitation of their article is the absence of an error analysis. The BERT model (bidirectional representation from transformers), which is used by Google's search engines, was developed by [PERSON] et al. [9]. Unlike recent language  
Figure 3: An example of coreference annotation in used corpus. representation models, they designed BERT to pre-train deep bidirectional representations from unlabeled text by joint conditioning on both the left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inferencing [1; 2; 3] without substantial task-specific architecture modifications. The results were spectacular; the GLUE score was 80.5%, and the MultiNLI accuracy was 86.7%. The Stanford question answering system dataset (SQuAD) v1.1 resulted in an F1 score of 93.2% and also a SQuAD v2.0 Test F1 score of 83.1%. [PERSON] et al. [10] investigated two strategies for improving coreference resolution. The first one involved training separate models that specialized in particular types of mentions (e.g., pronouns versus proper nouns), and the second one used a ranking loss function rather than a classification function. However accurate their models were at picking a correct antecedent for a true anaphor, the best they could achieve in terms of f-scores was 88.1% with MUC, 85.2% with B3, and 79.7% with CEAF. [PERSON] et al. [11] focused on anaphora resolution in English texts. Their approach improves on the traditional algorithm that is considered the standard baseline for comparison in the literature (by the Hobbs algorithm). Whilst the most significant contribution is provided by a gender agreement feature, the modification to the general rules alone already yields an improvement, for which they propose to use their algorithm as the new baseline in the literature. [PERSON] et al. [12] identified all types of anaphora with layered or step by step approaches so that everyone utilized the anaphora paradigm in their application. Their results presented an improved framework that performed all the necessary rules for distinguishing pronouns. [PERSON] et al. [13] introduced a conceptual graph model, which proposed a concept-based graph model that follows a triplet representation with coreference resolution, which extracts the concepts at both the sentence and document level. The extracted concepts are clustered using a modified DB Scan algorithm that then forms a belief network. Their model had an accuracy of 91%. [PERSON] et al. [14] proposed a machine learning approach using support vector machines (SVM) towards coreference resolution at the document level.