# Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User
## 2 Background
### Gender Identification Techniques  
Recently, the development of gender classification techniques has become more interactive, draws increasing interest and attracts researchers from various scientific disciplines. The application of classification techniques strives to extract characteristics that differentiate between masculinity and femininity. The primary goal is to automatically identify a person's gender according to the available data. Based on the adopted data type, we can categorize the proposed approaches into four classes: (1) Text Analysis-Based Approaches, (2) Face Recognition-Based Approaches, (3) Speech Recognition-Based Approaches, and (4) Biometrics-Based Approaches. A brief review of gender identification approaches that are based on text analysis follows:  
Since most information on the web is in textual form, it is reasonable for it to receive the highest interest and carry the largest share of research proposals. Textual data could be found in several forms: authors' names, tweets, Facebook posts, messages, blogs, etc. Many researchers have proposed solutions to recognize the gender of the text authors'; most of them focus on the text available on online social networks. In 2015, [28] introduced combination approaches for gender classification with text mining techniques. It employs sociolinguistic-inspired text features to enhance the performance of text mining methods. Moreover, the authors of [29] have proposed integrating the text mining approach to examine author preferences for gender classification tasks. Searching for linguistic information in the author's preferences could improve the performance of the gender classification. Therefore, integrating the self-assigned gender into the binary GI classifier could improve the accuracy of the results. The authors of [30] have introduced a supervised learning approach called the \"Cascading Transformer\" to identify the gender of named entities in text. Sequence labeling considering the context where the name appears outlines their solution for inferencing the gender; \"Female\", \"Male\", \"Ambiguous\", and \"Other\" represent the possible labels. The proposed model has been tested and evaluated on four open-source datasets created by the authors. Although most researchers tend to develop solutions to the English text, several research works have been proposed to handle other languages such as Arabic [31; 32; 33], Russian [34; 35], and Portuguese [36]. The authors of [32] have introduced a language-specific algorithm based on N-Gram Feature Vector that deals with Egyptian dialect. They achieved the text classification task using a Mixed Feature Vector, which was applied to the authors' annotated dataset of Egyptian dialects. Feature weights were applied using the Random Forest with Mixed Feature Vector and Logistic Regression with N-Gram Feature Vector. Further, in 2020 [33], the authors employed deep learning techniques for handling the gender identification task. They have investigated several Neural Network varieties (e.g., Convolutional Neural Networks, Convolutional Bidirectional Long-Short Term Memory, Long-Short Term Memory) on Arabic text on Twitter, more specifically, Egyptian dialect. The best-suited model was Convolutional Bidirectional Gated Recurrent Units, which achieved the highest accuracy. Turning to the Russian language, the authors of [34] have investigated the application of conventional machine learning models (e.g., Support Vector Machine, Gradient Boosting, Decision Tree) and varieties of Neural Networks (e.g., Convolutional Neural Network, Long-Short Term Memory) on the Russian text with gender deception to handle the task of GI. They examined several combinations and proposed the best four models that achieved high accuracy. Concerning the Portuguese language, the authors of [36] have proposed applying machine learning techniques to Portuguese on Twitter. They incorporate the extracted Meta-Attributes from the text with the ML algorithms for recognizing the linguistic expression related to gender.