# A Rule-Based Approach to Embedding Techniques for Text Document Classification
## 1 Introduction  
There has been an urgency in terms of classifying the information available online in the past ten years, prompting researchers to focus on automatic text classification (ATC). A widely used research method for this problem depends on rule-based and embedding techniques. In the 1960s, rule-based approaches began to emerge; however, they became more common in the 1970s and 1980s [1]. The late 1980s witnessed the formation of same-time or concurrent operations and activation of rules within production systems, all of which carried on into the following decade. The rule-based system includes a set of rules that can be implemented for many purposes, including the support of decision-making or for a predictive decision in real implementations. It is possible to divide the methods of creating rules into the categories of 'conquer and separate' [2] and 'divide and conquer' [3]. This produces categorization rules in the intermediate form of a decision tree, such as C4.5, C5.0, and ID3 [2]. In the same manner, ID3 is a covering technique [4] with an approach in the form of 'if then' rules. The structure of the systems of rule-based methods depends on logic-specific types, such as deterministic logic, fuzzy logic, and probabilistic logic. It can also divide the system of rule-based into the following types: systems of fuzzy rule-based, deterministic rule-based and probabilistic rule-based and determine rule-based systems being in the context of bases of the rule, which includes bases of modular rules, single rules, and chained rules [5]. In practice, the task of ensemble learning may be performed in a parallel form, in a distributed manner or on mobile platforms according to given computing environments. Finally, rule-based systems are divided into three types, namely distributed, mobile and parallel [6]. The Reuters-21578 newswire benchmark and 20 Newsgroups are the most widely used benchmarkcorpora in the research community in the text categorization field. They can be found in comparative studies of different approaches using flat (i.e., non-hierarchical) category systems in this corpus [7]. Hierarchical text classifiers are among the first works in this field, experiments with two classifiers on the subset of the Reuters collection reported by [PERSON] and [PERSON] [8]. Our rule-based and embedding models contributed to classifying the categories of Reuter's dataset such as (Acq, Corn, Crude, Earn, Grain, and Ship) according to their contents. The objective of this manuscript is to provide deeper information about the performance of embedding to rule-based text classification. The main research question to explore is how varying rule-based affects the performance of text classification and we investigate the performance differences when combining our rules-based and one of the embedding models such as (doc2 vec) in the task of text classification on the different datasets. We will implement many steps to acquire robust rules-based and embedding models using the Reuter 21578 and 20 Newsgroup corpora so as to make text categorization (TC) easier. Finally, Figure 1 summarizes the essential steps for a rule-based approach.