AIMC Topic: Language

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A syntactic evidence network model for fact verification.

Neural networks : the official journal of the International Neural Network Society
In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use t...

Unveiling factors influencing judgment variation in sentiment analysis with natural language processing and statistics.

PloS one
TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, ...

Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news.

PloS one
This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved que...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Scientific reports
Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015,...

Emotion recognition for human-computer interaction using high-level descriptors.

Scientific reports
Recent research has focused extensively on employing Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNN), for Speech Emotion Recognition (SER). This study addresses the burgeoning interest in leveraging DL for SER, specifi...

Multi-grained visual pivot-guided multi-modal neural machine translation with text-aware cross-modal contrastive disentangling.

Neural networks : the official journal of the International Neural Network Society
The goal of multi-modal neural machine translation (MNMT) is to incorporate language-agnostic visual information into text to enhance the performance of machine translation. However, due to the inherent differences between image and text, these two m...

Prompts, Pearls, Imperfections: Comparing ChatGPT and a Human Researcher in Qualitative Data Analysis.

Qualitative health research
The impact of ChatGPT and other large language model-based applications on scientific work is being debated across contexts and disciplines. However, despite ChatGPT's inherent focus on language generation and processing, insights regarding its poten...

ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition.

PloS one
Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different wri...

AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.

Scientific reports
Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English...

Natural language sentiment as an indicator of depression and anxiety symptoms: a longitudinal mixed methods study.

Cognition & emotion
The study tested how the use of positive- (e.g. beautiful) and negative-valenced (e.g. horrible) words in natural language and its change in time affects the severity of depression and anxiety symptoms among depressed and non-depressed individuals. T...