## 2 Related Work  
This section intends to provide insight into what has been done in terms of clustering with NLP data. Since the context of this work is particular, approaches from multiple fields will be explored, and a critical analysis of each one will be performed to understand if it could be applied to the GDM context. Many approaches were made before clustering on natural language text to achieve multiple objectives. [PERSON] et al. [15] applied clustering with NLP in the biology field by extracting data from two diverse sources, microarray gene expression data and gene co-occurrences in the scientific literature from bioRxiv using NLP. After normalizing the microarray data and applying dimensionality reduction with principal component analysis (PCA), they grouped this data into clusters using the K-means technique. The resulting clusters were compared to the extracted gene co-occurrences pairs in the NLP data to evaluate the results of the steps taken. The evaluation was done using entropy analysis on the combined data, comparing it to the maximum entropy from the sole clusters. Their results approve the usage of NLP in this field to extract gene co-occurrences from the literature in which the use of clustering helped confirm this claim. Although the approach shows an excellent combination of both areas, it is not what it is intended with this project, since the goal is to cluster unstructured text and not structured data, that was, in their case, the microarray gene expression data. [PERSON] et al. [16] applied clustering with NLP as an intermediary step in creating a model to predict occupational accident risk. After extracting the data from an integrated steel plant's safety management system database, pre-processing is done where duplicates, missing data, and inconsistent data are removed. The authors used EM-based text clustering to build clusters with categorical attributes while using the silhouette coefficient to determine the optimal number of clusters. These data are then fed to a deep neural network (DNN) model, with a structure comprised of a stacked autoencoder (SAE) with an autoencoder (AE) and a SoftMax classifier. The AE is a feed-forward artificial neural network (ANN) comprising one input layer, one hidden layer, and one output layer. Usually, it is trained to copy its input to its output so that the errors become minimum. Therefore, the dimension of the input must be the same as that of the output. Support vector machine and random forest was used to compare this approach. For DNN, the grid search technique was used to find the best hyperparameters. This approach shows one usage of clustering to categorize unstructured data, finding hidden connections between them. [PERSON] and [PERSON] [17] applied clustering with NLP in the medical field as an intermediary step in creating a model to predict diseases based on symptoms. The data are collected using medical forums about various stomach disease symptoms, and an OWL file is created. After data preprocessing, stopwords, stemming words, special characters, numbers, and white spaces were removed. Speech tagging is used to extract verbs, nouns,subjective words, adverbs, etc., from the dataset, so afterward, Fuzzy c means can cluster the data into groups of common symptoms. RDF is then utilized for taxonomic relations, object relations, and data, while OWL is used for attribute relations. These relations are then mapped for the genetic algorithm to predict the disease of a customer based on the symptoms. This approach uses many steps that could be utilized in this project. However, having an intra-sentence segmentation step in our project, the usage of fuzzy c-means becomes less needed. Most of the data will be treated so that it can only be part of one cluster, removing the need for soft clustering techniques. [PERSON] and [PERSON] [18] applied clustering with NLP in education to obtain meaningful information from student surveys.