Qatar University studies the use of neural network models

Learn to understand and interpret human languages ​​by simulating the human mind
Umama Abdul Rahman Hamad, a graduate with a master’s degree in computer science from the Department of Computer Science and Engineering, Qatar University (QU), recently spoke about her graduation thesis.
The thesis was titled “A study of the use of neural network models that simulate the human mind in learning to understand human languages”. Dr. Khaled Shaaban, professor of computer science, supervised the thesis.
The study that explores the use of neural network models that reproduce the human mind in the way of learning to understand and interpret human languages ​​is the most important aspect of this research thesis. Pre-processing textual data and translating it into a digital form so that these neural networks can understand and process it is necessary for these neural networks. Therefore, understanding this digital representation underlies many of the implicit natural language processing activities that people perform, such as writing text and responding to human queries.
In her statement, Umama Abdul Rahman, an MSc graduate in computer science, said: “One of the tasks of this study is to find the best ways to create a human-like Chabot that deliberately engages in dialogue by interpreting feelings and responding to users. ask questions kindly. Emotions are divided into more than thirty categories, and each emotion has a different intensity, which makes some emotions close to each other. There are several challenges that this study has addressed, first:
There are few studies and methods aimed at understanding and responding correctly to feelings in the Arabic language, compared to the English language, so this study has suggested a model that solves this problem. There is a fine line between text belonging to different sentiments due to vernacular dialects and different ways of expressing them.
Umama added, “There are lyrics that the way of expressing them can be positive, for example, but they can convey different emotions, like: happiness, surprise or amusement. Also, some feelings can introduce noise into the model, which can be expressed positively or negatively. Therefore, this study proposes a model called Sentiment Experts, which is a comprehensive model for Chabot by understanding feelings in general if the text belongs to one of the following categories: positive, negative or neutral and then analyzing deep feelings, such as as: happiness, pleasure, surprise, sadness or resentment. Extensive experiments and various model performance evaluations were conducted that demonstrated the effectiveness of the proposed approach in improving the accuracy of emotion detection and the relevance of the texts generated from this model.
Umama confirmed that this study is beneficial in several areas, such as: The presence of a Chabot in mental health can play the role of responding with appropriate answers to questions that the user may ask about the Chabot, because the answer that The result has a fundamental effect on the interlocutor, such as making the interlocutor feel better and thus improving their mental health.
The graduate also highlighted the most important ways to achieve his ambitions, saying, “I first seek to deepen my knowledge in my area of ​​specialization, which is natural language processing and machine learning, by reading the latest research in this area, then working on developing the model proposed in my master’s thesis to address all dialects of Arabic and publishing research in international journals and conferences to highlight its importance.


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