# A Rule-Based Approach to Embedding Techniques for Text Document Classification
## 2 Related Work  
Various studies relating to classification have been carried out taking a number of approaches. In classification systems, a rule-based learning approach to text categorization is utilized. [PERSON] and [PERSON] suggested using rule-based methods selectively depending on the nature of the information to be extracted and make comparisons with the machine learning [9]. The model of rule-based learning consists of a set of rules learned from data [10]. [PERSON] introduced an integrated framework for the design of systems of rule-based to implement missions of categorization, which included the process of rule representation, rule generation, and rule simplification. The study stressed the importance of the combination of different types of algorithms of rule learning techniques via ensemble learning [5]. Rule-based machine translation (RBMT) considers the unclear points pertaining to morphology and lexicon as serious challenges. A contribution by [PERSON] and [PERSON] [11] describes an approach to resolving the forms of the morphologically ambiguous verb if a rule-based decision is not possible  
Figure 1: Steps of rule-based processing.  
due to tagging errors or parsing. [PERSON] et al. developed an automated patient portal message classifiers with the rule-based approach using natural language processing (NLP) and the bag of words [12]. [PERSON] et al. discuss different automatic spike detection methods in order to improve detection performance and establish a user-adjustable sensitivity parameter mainly by examining the functioning of a rule-based system, artificial neural networks (ANNs) and random forests [13]. Accordingly, the rule-based system needed a feature selection to classify text documents. Feature selection can be performed by following one of three approaches, i.e., filter, wrapper or embedded approaches [14]. In this study, this depends on embedded methods to select a feature. The optimal parameters are learned by using the embedded method to perform the feature selection approach [15]. INRA (Institut national de la recherche agnomique) and Cnrs (Centre national de la recherche scientifique) at University Paris Saclay proposed a two-step method to normalize multi-word terms with concepts from a domain-specific ontology. In this method, they used vector representations of terms computed with word embedding information and hierarchical information from ontology concepts [16]. [PERSON] and [PERSON] presented word2 vec and later introduced the doc2 vec algorithm based on adjusted techniques for learning how to embed texts identical to word2 vec, thus turning doc2 vec into a branch of word2 vec [17]. In their work, doc2 vec was applied to model embedding for text categorization. The motivation of this study was to classify text documents by taking a rule-based approach to embedding techniques and this work will assist us in determining the acceptable methods to follow for in-text categorization based upon measuring the related criteria. Finally, this manuscript comprises eight sections containing all the necessary information related to the rule-based approach using automatic text classification for the top ten categories of the Reuters-21578 and 20 Newsgroups data sets. Then, the paper is structured as follows: Section 1 introduces the rule-based and text classification approaches. Section 2 presents related work with the study. Section 3 explains in detail the research methodology. Section 4 presents the data analysis and results. Section 5 discusses the study results. Finally, Sections 6-8 provide a conclusion, future research directions and limitations