Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability by Anne Lauscher, Pablo Ruiz Fabo, Federico Nanni, Simone Paolo Ponzetto

“…While some of these resources have already been adopted by the natural language processing community, they still hold a large potential for humanities scholars, part of which could be exploited in studies that will adopt the fine-grained exploration method presented in this paper.…”
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Survey on Organizational Chat Conversation Analysis: Exploring Dialogue Summarization from a Knowledge Discovery Perspective by Ksenija Kosilova, Ilze Birzniece

“…With the latest advances in natural language processing technologies, multi-participant conversation summarization features are now embedded in the most widely used collaboration platforms offered by industry leaders such as Microsoft, Google, and Zoom. …”
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Cross-Linguistic Correspondences and Current Problems in Terminography by N. K. Riabtseva

“…A wide collection of corpus data, compiled for the last five years (over 1 mil. words) out of leading authentic journals and books in English on Natural Language Processing (NLP) and corresponding authentic texts in Russian, provided with translation of titles, abstracts and key words from Russian into English, as well as the analysis of computer translation systems, shows that contemporary English-Russian dictionaries and digital lexicographic resources need serious improvement. …”
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Virtual Teams: A Smart Literature Review of Four Decades of Research by Takuma Kimura

“…Based on the smart literature review framework, this study harnesses artificial intelligence techniques, specifically natural language processing and topic modeling, to extensively analyze the trends in virtual team research spanning the last four decades. …”
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What Matters in Irony Detection: An Extended Feature Engineering for Irony Detection in English Tweets. by Linrui Zhang, Qixiang Pang, Belinda Copus

“…In recent years, large-scale language models (LLMs) have nearly become the dominant force in almost every natural language processing (NLP) task. The primary research approach has focused on selecting the most appropriate language model for specific NLP tasks and then incorporating linguistic features to enhance the model’s performance. …”
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Towards Cross-lingual Representation of Prototypical Lexical Knowledge by Francesca Grasso, Luigi Di Caro

“…In order to be concretely effective, many Natural Language Processing (NLP) applications require the availability of lexical resources providing varied, broadly shared, and language-unbounded (i.e., not limited to any specific language or linguistic system) lexical information. …”
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Understanding Counterspeech for Online Harm Mitigation by Yi-Ling Chung, Gavin Abercrombie, Florence Enock, Jonathan Bright, Verena Rieser

“…This paper aims to fill this gap by systematically reviewing counterspeech research in the social sciences and comparing methodologies and findings with natural language processing (NLP) and computer science efforts in automatic counterspeech generation. …”
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Decoding Romanian labour protests: insights from a topic modeling approach by Ciprian Panzaru, Gabriela Negoita

“…This article provides an in-depth exploration of recent labour mobilisations, focusing on the industrial actions that took place between 2021 and 2022, a period marked by post-COVID-19 economic recovery and legislative reforms. Using natural language processing (NLP) techniques, this study analyses press releases from Romania’s official news agency to identify recurring themes and patterns in union-led protests. …”
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Towards Intelligent Assessment in Personalized Physiotherapy with Computer Vision by Victor García, Olga C. Santos

“…Preliminary results indicate that YOLO Pose provides reliable pose estimation, offering a solid foundation for future enhancements, such as the integration of natural language processing (NLP) to improve patient interaction through empathetic, AI-driven support.…”
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Sentiment Matters for Cryptocurrencies: Evidence from Tweets by Radu Lupu, Paul Cristian Donoiu

“…Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing techniques on tweets from influential Twitter accounts. …”
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Engaging Stakeholders With Professional or Lived Experience to Improve Firearm Violence Lexicon Development by Nicole Cook, Frances M Biel, Kerry Ann Bet, Marion R Sills, Ali Al Bataineh, Pedro Rivera, Anna R Templeton, Natalie Cartwright

“…Electronic health record clinical notes, when leveraged through natural language processing (NLP), are a potential source of data on firearm exposure. …”
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Bioregulatory event extraction using large language models: a case study of rice literature by Xinzhi Yao, Zhihan He, Jingbo Xia

“…Abstract The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corpus. …”
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A rule-based stemmer for Arabic Gulf dialect by Belal Abuata, Asma Al-Omari

“…Dialects add a new set of variational dimensions in some fields like natural language processing, information retrieval and even in Arabic chatting between different Arab nationals. …”
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An automatic pipeline for temporal monitoring of radiotherapy-induced toxicities in head and neck cancer patients by Parsa Bagherzadeh, Khalil Sultanem, Gerald Batist, Shirin Abbasinejad Enger

“…To reduce the burden of manual assessment of toxicities, we propose a language processing model for the automatic extraction of toxicities. …”
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Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States. by Jackson Bennett, Benjamin Rachunok, Roger Flage, Roshanak Nateghi

“…This paper outlines a machine learning framework-grounded in statistical learning theory and natural language processing-to augment climate change opinion surveys with social media data. …”
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BERT Mutation: Deep Transformer Model for Masked Uniform Mutation in Genetic Programming by Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf

“…We introduce BERT mutation, a novel, domain-independent mutation operator for Genetic Programming (GP) that leverages advanced Natural Language Processing (NLP) techniques to improve convergence, particularly using the Masked Language Modeling approach. …”
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From feedback to Insight: Uncovering gender bias in STEM evaluations through NLP by Enrique Diaz de Leon Lopez

“…Our study uses Natural Language Processing (NLP) to analyze 70,000 student evaluations from a Latin American STEM university, revealing statistically significant disparities in how students assess male and female professors and exposing implicit bias in qualitative feedback. …”
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Educational Data Mining: A Foundational Overview by Ilias Papadogiannis, Manolis Wallace, Georgia Karountzou

“…Some key methodologies addressed include classification, regression analysis, clustering techniques, association rule mining, and Natural Language Processing, among others. Additionally, this paper looks at how EDM can facilitate data-driven decision-making among other areas such as curriculum development and customization of learners’ experiences. …”
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Impact of Artificial Intelligence on Traditional Accounting - An Overview of Opportunities and Challenges by Tang Xiaogeng

“…In addition to financial automation, auditing, tax compliance, and predictive analysis, advanced AI technologies like machine learning (ML), robotic process automation (RPA), and natural language processing (NLP) are becoming more and more integrated into various accounting functions. …”
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An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari

“…Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”
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