###### Abstract  
Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated language processing. Slot filling and intent detection are significant modules in task-based dialogue systems. Intent detection is a critical task in any natural language understanding (NLU) system and constitutes the base of a task-based dialogue system. In order to build high-quality, real-time conversational solutions for edge gadgets, there is a demand for deploying intent-detection methods on devices. This mandates an accurate, lightweight, and fast method that effectively operates in a resource-limited environment. Earlier works have explored the usage of several machine-learning (ML) techniques for detecting intent in user queries. In this article, we propose Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for natural language understanding. The presented CL-DLBIDC technique receives word embedding as input and learned meaningful features to determine the probable intention of the user query. In addition, the presented CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm was used in this study. The experimental analysis of the CL-DLBIDC method took place under a set of simulations, and the results were scrutinized for distinct aspects. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC algorithm over other DL models. computational linguistics; deep learning; natural language understanding; intent detection; mayfly optimization +
Footnote †: journal: _Sciences_  
inputs into semantic slots which have been previously defined [2]. Two modules, such as slot filling and intent detection, could transform the text into a semantic representation that offers the task data to support the dialogue system and aids users in achieving their goals [3]. \"Intent detection\" refers to the task of categorizing natural language utterances into semantic intent classes that have previously been defined [4]. Finding message intent in natural language utterances becomes a critical task for conversational mechanisms [5]. In applications ranging from natural-language response production to offering intellectual proposals, understanding the primary intent of a context interaction becomes critical. Conventionally, research on intent detection has focused on this task with the supposition that inference and training are executed using cloud infrastructure or well-equipped servers [6; 7]. This results in the inappropriateness of prevailing machine-learning (ML) techniques for real-time dialogue mechanisms in low-resource edge devices because of its higher latency and dependence on large, pre-trained methods. Recently, there has been greater commercial and academic interest in guiding artificial intelligence (AI) solutions that could work in a straightforward manner on a device using local data [8; 9]. On-device AI methods are capable of supporting intent detection in real-time at a low latency and aid in guarding the privacy of delicate user data, such as smartphone messages. Slot filling and intent detection have always been implemented as the pipeline modules in research on conventional, task-based dialogue systems [10]. However, pipeline techniques propagate mistakes effortlessly. In contrast with pipeline techniques, joint methods have the benefit of using the dependency between slots and intent. Such works have been primarily classified into two classes: the use of semantic analysis and a joint model that incorporates both classification and feature design into the learning process [11].