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Linguistic Intelligence in Large language Models for Telecommunications

Linguistic Intelligence in Large Language Models for Telecom...

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ieee international conference on Communications Workshops, ICC

作者： Tasnim Ahmed Nicola Piovesan Antonio De Domenico Salimur Choudhury School of Computing Queen's University Ontario Canada Paris Research Center Huawei Technologies Boulogne-Billancourt France

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Large language Models (LLMs) have emerged as a significant advancement in the field of natural language processing (NLP), demonstrating remarkable capabilities in language generation and other language-centric tasks. Despite their evaluation across a multitude of analytical and reasoning tasks in various scientific domains, a comprehensive exploration of their knowledge and understanding within the realm of natural language tasks in the telecommunications domain is still needed. This study, therefore, seeks to evaluate the knowledge and understanding capabilities of LLMs within this domain. To achieve this, we conduct an exhaustive zero-shot evaluation of four prominent LLMs-Llama-2, Falcon, Mistral, and Zephyr. These models require fewer resources than ChatGPT, making them suitable for resource-constrained environments. Their performance is compared not only with state-of-the-art, fine-tuned models but also with larger LLMs such as GPT-3.5 and GPT-4. To the best of our knowledge, this is the first work to extensively evaluate and compare the understanding of LLMs across multiple language-centric tasks in this domain. Our evaluation reveals that zero-shot LLMs can achieve performance levels comparable to the current state-of-the-art fine-tuned models. This indicates that pretraining on extensive text corpora equips LLMs with a degree of specialization, even within the telecommunications domain. We also observe that no single LLM consistently outperforms others, and the performance of different LLMs can fluctuate. Although their performance lags behind fine-tuned models, our findings underscore the potential of LLMs as a valuable resource for understanding various aspects of this field that lack large annotated data.

关键词： Large language models conferences Linguistics Chatbots Data models Cognition Telecommunications

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Artificial Intelligence based Chatbot for promoting Equality in High School Advising 4

Artificial Intelligence based Chatbot for promoting Equality...

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4th international conference on Intelligent engineering and Management, ICIEM 2023

作者： Assayed, Suha Khalil Alkhatib, Manar Shaalan, Khaled The British University in Dubai Faculty of Engineering and Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350341126

The sustainable development goal 4 (SDG4) aims to 'ensure inclusive and equitable quality education and promote lifelong learning opportunities for all', Therefore., researchers are inspired to study the fairness and equality in different aspects of education. Some studies are focused on social and academic initiatives and others on developing state-of-the-art technology to enhance the education between students equally. Henceforth., in this paper a novel affordable chatbot implemented by using a neural network model and natural language processing (NLP) to assist students in high schools particularly., since high school is one of the most essential stages in students' lives., as in this stage., students have the option to select their academic streams and advanced courses that can shape their career with their passions and interests. The dataset in this study collected from different academic resources such as schools & universities websites., schools' advisers., parents and students. It includes (968) pairs of enquiries and tags. The first column represents the student's enquiry;the second column indicates the tag or the class of each sentence. The model built by connecting the input data into embedding layer., and then the data fed into the LSTM layer with different number of neurons., then authors used sigmoid function for the output layer. The result in this study shows that the performance of the chatbot is improved by increasing the number of neurons from 5 to 8, the model achieved high accuracy ratio with score (96.5%). In future the model will be developed with stacked LSTM layers with using softmax activation function in the output layer, as different classes will be added as well in the dataset. © 2023 ieee.

关键词： Students

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SMS Spam Classification Using Machine Learning Techniques 12

SMS Spam Classification Using Machine Learning Techniques

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12th international conference on Cloud Computing, Data Science and engineering, Confluence 2022

作者： Jain, Tarun Garg, Payal Chalil, Namita Sinha, Aditya Verma, Vivek Kumar Gupta, Rishi Scit Manipal University Jaipur India Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9781665437011

Almost every person today owns a mobile phone with at least the most basic facilities like messaging and calling. Spam calls are already infamous for the constant ringing of cell phones for promotional or fraudulent pitching to innocent customers. With the reducing costs of bulk messaging services from network providers, a massive base of these spam calls has shifted to messaging. SMS, standing for a short messaging service, has become a dumping ground of unwanted product descriptions and scam offers. Here, in this scenario, classification becomes a necessity. Classification in this context occurs as separating spam messages from 'ham' or legitimate messages. For this aforementioned purpose, we used natural language processing techniques and machine learning algorithms in this paper. We used four simple classification models on a dataset from UCI Machine Learning Repository. We compared the accuracies at the end, which pointed towards the most suitable model being SVM, with an accuracy of 98.797%. © 2022 ieee.

关键词： Classification (of information)

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Hindi Document Extractive Summarization: Neural Method on A New Data Set 5

Hindi Document Extractive Summarization: Neural Method on A ...

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5th international conference on Computational Intelligence and Networks, CINE 2022

作者： Tawatia, Kunal Jain, Nishant Kundu, Suman Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur342037 India

ISBN: (纸本)9781665464659

Extractive summarization is one of the vital tasks in text analysis and natural language processing. Although Hindi is one of the world's highly speaking languages and produces thousands of online documents daily, most existing text summarization works focus on the English language. A Neural network-based summarizer is popular for abstractive summarization but has not been explored for extractive one except in a few recent studies. The present work uses a neural extractive summarizing model to develop a Hindi language extractive summarizer. The main contribution of the paper is two-fold. First, we generated a new Hindi-based text summarization data set from a popular Hindi news channel AajTak. The code to generate the data set is available at https://***/sonaa-hindi-text. Then we use this data set to train a Neural Extractive Summarization model. The model also learns the word embeddings while learning itself. The ROUGE-2-Fl and ROUGE-1-Fl results on test data show promising output with a score of 20.02 and 39.81, respectively. © 2022 ieee.

关键词： Text processing

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Using Group Deep Learning and Data Augmentation in Persian Sentiment Analysis 8

Using Group Deep Learning and Data Augmentation in Persian S...

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8th international Iranian conference on Signal processing and Intelligent Systems, ICSPIS 2022

作者： Nazarizadeh, Ali Banirostam, Touraj Sayyadpour, Minoo Islamic Azad University Department of Computer Engineering Central Tehran Branch Tehran Iran Kharazmi University Tehran Iran

ISBN: (纸本)9781665476232

Sentiment analysis is one of the most important areas in natural language processing, which investigates people's opinions on products, services, and various events. The number of papers published on Persian sentiment analysis is much less than in other languages such as English, and this made our research in Persian use different approaches to boost the performance of Persian sentiment analysis. In this research, Group Deep Learning and Data Augmentation approach has been used to classify emotions in Persian, which has not been used in any previous research to analyze Persian emotions. After applying the Data Augmentation technique and using Group Deep Learning approach for classification, we achieved 96.5% accuracy in Persian sentiment analysis. © 2022 ieee.

关键词： Sentiment analysis

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Emotion Recognition from Mizo Speech: A Signal processing Approach

Emotion Recognition from Mizo Speech: A Signal Processing Ap...

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2022 ieee international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Mangalam, Aditya Raj Singh, Sudeep Lalremtluanga, C. Kumar, Prakash Das, Ranjita Basu, Joyanta Chatterjee, Saibal National Institute of Technology Mizoram Department of Electrical and Electronics Engineering Aizawl India National Institute of Technology Mizoram Department of Computer Science and Engineering Aizawl India Cdac Kolkata Kolkata India

ISBN: (纸本)9781665483162

As humans, the most natural way for expressing feelings and communicating is verbal speech. Communication with machines is done either by traditional input methods i.e., keyboard, joystick or with the help of speech recognition technology. The speech recognition mostly works on an average input of user but it lags the instant response, based on the mood of the user and the emotional feature of communication. Emotion recognition is one of the most popular and expanding research domain in recent years. The world is moving towards the functioning of machines with less manual input. Human-computer interaction can be improved by emotions recognition, reducing the need for human intervention. The paper presents an emotion recognition system developed by utilizing the different machine learning techniques that helps to process and classify speech signals to detect emotions. The input to the system are speech audios that were recorded in the Mizo language, the native language of Mizoram, India. To develop the emotion recognition system, seven basic emotions i.e., Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise, were analyzed from emotional speech signals. Further, to extract the features from audio signals, signal processing methods have been used. In this work twelve features were extracted, those are Autocorrelation, Chroma, Fundamental frequency, Zero-crossing rate, Short-time energy, RMS, Periodogram, LPCC, MFCC etc. The extracted features were used to train four classifiers namely Decision tree, KNN, SVM and Ensemble. At last, a model was prepared with the combination of classifiers that recognizes the emotion of the signal. The emotion recognition system recognizes single emotions as well as mixed types of emotions. The insights gained may be helpful in a wide range of applications. © 2022 ieee.

关键词： Feature Selection

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Unsupervised Neural Machine Translation for English to Kannada Using Pre-Trained language Model 13

Unsupervised Neural Machine Translation for English to Kanna...

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13th international conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Sheshadri, Shailashree K Sai Bharath, B. Chandana Sarvani, Alapati Hari Naga Sree Bharathi Reddy, Potta Reddy Vijaya Gupta, Deepa Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

With the advent of Neural Networks, Neural Machine Translation (NMT) has been a booming area of research in natural language processing. Indic languages possess less significant parallel and monolingual corpus than English and other European languages. Even in Indic languages, many Indo-Aryan and Dravidian languages fall into low-resource languages, and techniques like Back Translation, Unsupervised Neural Machine Translation (UNMT), and Transfer Learning help achieve better translation accuracy. This paper uses the UNMT approach with a pre-trained Cross-Lingual language Model (XLM) for the English to Kannada language pair. Our proposed methodology has achieved a 0.61 BLEU score in English to Kannada and a 0.32 BLEU score in Kannada to English translation. © 2022 ieee.

关键词： Neural machine translation

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A Computational Approach to Classify Empathy Expressed in Text-Based Student Reflections

A Computational Approach to Classify Empathy Expressed in Te...

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ieee international conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Sanjana Dyavappanavar Radhika Amashi Vijayalakshmi M. School of Computer Science and Engineering KLE Technological University Hubballi India Centre for Engineering Education Research KLE Technological University Hubballi India

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

Empathy is the ability to understand and share the feelings or perspectives of others [1]. Empathy is a complex psychological and emotional concept that involves understanding and sharing the feelings, thoughts, and perspectives of others. In the realm of engineering education, understanding and fostering empathy among students is paramount for promoting social and emotional learning [2]. While there are studies in literature, where researchers and educators are trying new pedagogies to foster empathy in engineering students, it is often not reported on how can empathy be identified from their artifacts. Therefore, this study aims to use the existing RoBERTa model (application of natural language processing) for predicting the type of empathy exhibited by students on real-world problems in the large corpus of 550 reflective essays. This study addresses the following research question:How does the proposed model help in predicting the 3 levels of empathy in student’s reflections?The anticipated outcome of this study is to find three distinct categories of empathy: cognitive, affective, and prosocial behavior, and classify students’ written reflections to align with these categories. By harnessing the power of advanced language models, we aim to automate the classification of empathetic responses, shedding light on the empathetic inclinations of students. The results hold promise for educators, offering insights into students’ empathetic responses to real-world issues, thereby enabling the design of interventions and engineering curricular enhancements. As engineering education continues to emphasize empathy as a vital skill, this research presents a forward-looking approach to assess the empathetic responses of students in the modern educational landscape.

关键词： Psychology Predictive models Reflection natural language processing engineering students

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A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment Analysis 24

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

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24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and Systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Dang, Lanxue Wang, Chunyu Han, Hongyu Hou, Yan-E Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9798350319934

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset. © 2022 ieee.

关键词： Sentiment analysis

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Visual Encoding Method for Industrial VR Interface Prompts Supporting Well Drilling's Suction Process

Visual Encoding Method for Industrial VR Interface Prompts S...

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Image processing and Computer Applications (ICIPCA), ieee international conference on

作者： Fei Xiong Jun Xiao Rong Lu Feilong Han Zhuo Wang Shisong Chen Second Engineering Department Shanghai Foundation Engineering Group Co. Ltd Shanghai China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

To shorten the training time of well sinking construction personnel and enhance their operational proficiency, a visual encoding method for industrial VR interface prompts has been designed and developed to support the suction process during drilling. Based on the HTC VIVE Pro 2 external device and utilizing virtual reality and human-computer interaction technologies, this method achieves the training objectives for well sinking operations. Through natural interactive operations and visualized simulations of virtual events, it provides users with an immersive educational experience, reducing the gap between theoretical instruction and practical operations in traditional training models, and facilitating easy knowledge acquisition by users.

关键词： Training Drilling Human computer interaction Visualization Solid modeling Image coding knowledge acquisition Virtual reality Computer applications Personnel

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