Academic Articles & Research Papers

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Welcome to EMNLP 2010, Conference on Empirical Methods in Natural Language Processing! The conference is organized by SIGDAT, the Association for Computational Linguistics' special interest group on linguistic data and corpus-based approach...

Data augmentation approaches in natural language processing: A survey

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in...

Ten rules for robust taxonomy in ecological datasets.

Unify Framework for Crime Data Summarization using RSS Feed Service

This research presents online crime news analysis using text mining, Natural Language Processing framework (General Framework for Text Mining: GATE), and data warehouse (DW) technologies. The proposed framework aims at extracting key featur...

Development and External Validation of a Machine Learning Model for Automated Feedback Quality Assessment in Chinese Anesthesiology Residency Training

Lifeng Yao, Yijun Chen, Jing Shen, Junge Zhang, Yiwei Zhang, Guojin Liang, Yiqin Ji Department of Anesthesiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, People’s Republic of ChinaCorrespondence: Yijun Chen, De...

A systematic literature review on spam content detection and classification

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. ...

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Abstract Background Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integr...

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...

Target reindexing and residual reconstruction enable reliable retrieval augmented few shot recognition

Abstract Retrieval-augmented few-shot recognition supplements sparsely annotated target classes with external visual samples, but open retrieval results may exhibit unstable quality, semantic mismatch, and cross-domain feature shifts. Direc...

Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review

Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...

An Introduction to Natural Language Processing

Electronically stored clinical documents may contain both structured data and unstructured data. The use of structured clinical data varies by facility, but clinicians are familiar with coded data such as International Classification of Dis...

Natural Language Processing

This chapter starts with several generic sections about natural language processing (NLP) datasets and big-picture concepts. It focuses on the core NLP concepts, and quick-and-dirty techniques to more complicated ones. The most basic concep...

Neural transfer learning for natural language processing

The current generation of neural network-based natural language processing models excels at learning from large amounts of labelled data. Given these capabilities, natural language processing is increasingly applied to new tasks, new domain...

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be bes...

A new image similarity metric for a perceptual and transparent geometric and chromatic assessment

Abstract Full-reference image quality assessment metrics such as PSNR and SSIM misalign with human perceptual judgments on texture and shape distortions, a limitation documented across multiple benchmarks. Deep perceptual metrics such as LP...

Enhancing adult lifelong learning through data fusion algorithms and quality assurance

Abstract This study presents a Data Fusion + Random Forest (RF) framework to predict adult learners’ educational outcomes, such as dropout risk and course completion. Adult lifelong learning is crucial for personal and professional growth, ...

An Analysis of the Training Data Impact for Domain-Adapted Tokenizer Performances—The Case of Serbian Legal Domain Adaptation

Various areas of natural language processing (NLP) have greatly benefited from the development of large language models in recent years. This research addresses the challenge of developing efficient tokenizers for transformer-based domain-s...

Cracking the code: AI’s role in mapping evidence syntheses to the United Nations sustainable development goals

Abstract Background Understanding the alignment and contributions of research to the United Nations Sustainable Development Goals (UN SDGs) is essential for guiding global progress toward these critical targets. Several SDG mapping approach...

THE NORTHERN LABOR MARKET IN A RESOURCE-BASED REGIONAL ECONOMY: THE CASE OF THE KHANTY-MANSIYSK AUTONOMOUS OKRUG—YUGRA

This study examines the labor market of the Khanty-Mansi Autonomous Okrug–Yugra. The relevance of the research stems from the region’s strong dependence on natural resource reserves and production volumes, driven by the dominance of the min...

A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge

We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural langu...