Identification of Review Helpfulness Using Novel Textual and Language-Context Features by Muhammad Shehrayar Khan, Atif Rizwan, Muhammad Shahzad Faisal, Tahir Ahmad, Muhammad Saleem Khan, Ghada Atteia

“…In the research field of language understanding, categorization of movie reviews can be challenging because human language is complex, leading to scenarios where connotation words exist. …”
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Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions by Md. Saddam Hossain Mukta, Salekul Islam, Swakkhar Shatabda, Mohammed Eunus Ali, Akib Zaman

“…Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. …”
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Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning by Lifeng Han, Serge Gladkoff, Gleb Erofeev, Irina Sorokina, Betty Galiano, Goran Nenadic

“…This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. …”
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From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election by Olusola Olabanjo, Ashiribo Wusu, Oseni Afisi, Mauton Asokere, Rebecca Padonu, Olufemi Olabanjo, Oluwafolake Ojo, Olusegun Folorunso, Benjamin Aribisala, Manuel Mazzara

“…Conclusion: Sentiment analysis and other Natural Language Understanding tasks can aid in the understanding of the social media space in terms of public opinion mining. …”
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Enabling All In-Edge Deep Learning: A Literature Review by Praveen Joshi, Mohammed Hasanuzzaman, Chandra Thapa, Haithem Afli, Ted Scully

“…In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition, image processing, and natural language understanding. One of the significant contributors to the success of DL is the proliferation of end devices that act as a catalyst to provide data for data-hungry DL models. …”
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The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling by Mohammad Hamid Asnawi, Anindya Apriliyanti Pravitasari, Tutut Herawan, Triyani Hendrawati

“…In this paper, we propose an innovative approach to tackle this challenge by combining the Contextualized Topic Model (CTM) and the Masked and Permuted Pre-training for Language Understanding (MPNet) model. Our approach aims to create a more accurate and context-aware topic model that enhances the understanding of user experiences and opinions. …”
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Public perceptions of synthetic cooling agents in electronic cigarettes on Twitter. by Andrew H Liu, Julia Hootman, Dongmei Li, Zidian Xie

“…The deep learning RoBERTa (Robustly Optimized BERT-Pretraining Approach) model that can be optimized for contextual language understanding was used to classify attitudes expressed in tweets about synthetic cooling agents and identify e-cigarette users. …”
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Contextual Conversational Agent to Address Vaccine Hesitancy: Protocol for a Design-Based Research Study by Youness Zidoun, Sreelekshmi Kaladhara, Leigh Powell, Radwa Nour, Hanan Al Suwaidi, Nabil Zary

“…The research team is currently reviewing the natural-language understanding model as part of the conversation-driven development (CDD) process in preparation for the first pilot intervention, which will conclude the CA’s first design cycle. …”
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Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field by Zakaria Aoujil, Mohamed Hanine, Emmanuel Soriano Flores, Md. Abdus Samad, Imran Ashraf

“…While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. …”
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Joint inference in pragmatic reasoning by Bergen, Leon

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Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction by Liu, Jingjing, Ph. D. Massachusetts Institute of Technology

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Machine learning in dentistry by Yuen, Priscilla Li Jie

“…With the improvements in hardware like Graphical Processing Unit (GPU), DL has allowed advancements in a machine’s ability to conduct tasks like speech recognition, visual recognition, and language understanding [1,2]. Visual recognition is a huge feat for machines as they do not process images the way human eyes view and recognise a scene. …”
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Visual dialog system by Luong, Hien Nga

“…In this era of Artificial Intelligence, Large Language Models (LLMs) have emerged as powerful tools, facilitating the revolution of natural language understanding and generation tasks across different domains. …”
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Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy Than ICD Codes by Yuting Guo, Mohammed A. Al‐Garadi, Wendy M. Book, Lindsey C. Ivey, Fred H. Rodriguez, Cheryl L. Raskind‐Hood, Chad Robichaux, Abeed Sarker

“…Using 80% of the patient data, we trained and optimized multiple machine learning models, support vector machines and 2 versions of RoBERTa (a robustly optimized transformer‐based model for language understanding), for automatically identifying Fontan cases based on notes. …”
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Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension by Fernando Fernández-Martínez, Cristina Luna-Jiménez, Ricardo Kleinlein, David Griol, Zoraida Callejas, Juan Manuel Montero

“…Intent recognizers also often appear as a form of joint models for performing the natural language understanding and dialog management tasks together as a single process, thus simplifying the set of problems that a conversational system must solve. …”
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DIR: A Large-Scale Dialogue Rewrite Dataset for Cross-Domain Conversational Text-to-SQL by Jieyu Li, Zhi Chen, Lu Chen, Zichen Zhu, Hanqi Li, Ruisheng Cao, Kai Yu

“…The methodology of dividing a dialogue understanding task into dialogue utterance rewriting and language understanding is feasible to tackle this problem. …”
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<span style="font-variant: small-caps">inTIME</span>: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence by Paris Koloveas, Thanasis Chantzios, Sofia Alevizopoulou, Spiros Skiadopoulos , Christos Tryfonopoulos 

“…<span style="font-variant: small-caps;">inTIME</span> is a zero-administration, open-source, integrated framework that enables security analysts and security stakeholders to (i) easily deploy a wide variety of data acquisition services (such as focused web crawlers, site scrapers, domain downloaders, social media monitors), (ii) automatically rank the collected content according to its potential to contain useful intelligence, (iii) identify and extract cyber-threat intelligence and security artifacts via automated natural language understanding processes, (iv) leverage the identified intelligence to actionable items by semi-automatic entity disambiguation, linkage and correlation, and (v) manage, share or collaborate on the stored intelligence via open standards and intuitive tools. …”
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Meeting the Behavioral Health Needs of Health Care Workers During COVID-19 by Leveraging Chatbot Technology: Development and Usability Study by Maga Jackson-Triche, Don Vetal, Eva-Marie Turner, Priya Dahiya, Christina Mangurian

“…The chatbot is an algorithm-based, automated, and interactive artificial intelligence conversational tool that uses natural language understanding to engage users by presenting a series of questions with simple multiple-choice answers. …”
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Transfer learning and robustness for natural language processing by Jin, Di,Ph.D.Massachusetts Institute of Technology.

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Learning 3D Representations from Data by Wang, Yue

“…Deep models enable human-level perception, photorealistic image generation, and conversational language understanding. Despite significant progress, existing deep models still fail to meet the demands of robotics. …”
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