Academic Articles & Research Papers

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the mo...

National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis

Twitter is a powerful tool for world leaders to disseminate public health information and to reach citizens. While Twitter, like other platforms, affords world leaders the opportunity to rapidly present information to citizens, the discours...

Using 311 data to develop an algorithm to identify urban blight for public health improvement.

The growth of administrative data made available publicly, often in near-real time, offers new opportunities for monitoring conditions that impact community health. Urban blight-manifestations of adverse social processes in the urban enviro...

Weighted DAG Automata for Semantic Graphs

Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that pa...

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

Comparative Study of CNN and RNN for Natural Language Processing

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...

Natural Language Processing

Natural language processing is a fast-evolving field with popular applications in language translation, speech recognition, text classification, and summarization of text, among others. The central aspect of NLP is vectorizing text data. Te...

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the contex...

FREDC: A Few-Shot Relation Extraction Dataset for Chinese

Few-shot relation extraction (RE) is a critical task in natural language processing (NLP), yet benchmark datasets for non-English languages, particularly Chinese, remain scarce. Owing to their narrow focus and insufficient coverage of relat...

Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the o...

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BackgroundGiven the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews ...

A fault location method for secondary virtual circuits in smart substations based on the integration of GAE and GCN

Abstract The secondary virtual circuits in smart substations enable digital and networked information exchange between devices. However, their extensive logical connections, complex structure, and implicit interdependencies pose significant...

A Joint Model for Answer Sentence Ranking and Answer Extraction

The Effectiveness of Arabic Stemmers Using Arabized Word Removal

Other languages have influenced Arabic because of several factors, such as geographical nearness, trade communication, past Islamic conquests, science and technology, new devices, brand names, models, and fashion. As a result of these facto...

One Picture and One Thousand Words: Toward integrated multimodal generative models

Thanks to independent advances in language and image generation, we could soon be in the position to have systems that communicate with us by combining language and images in their output, a skill that humans do not possess (we receive, but...

Enhancing educational semantic retrieval through dual objective fine tuning of open source embedding models

Abstract Recent advances in AI have catalyzed the adoption of intelligent educational tools, yet many semantic retrieval systems remain ill-suited to the unique linguistic and structural characteristics of academic content. This study prese...

Paradigm Shift in Natural Language Processing

Abstract In the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as...

Adversarial Attacks on Deep-learning Models in Natural Language Processing

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulne...

Natural Language Processing

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are sy...

Natural Language Processing in Surgery

OBJECTIVE: The aim of this study was to systematically assess the application and potential benefits of natural language processing (NLP) in surgical outcomes research. SUMMARY BACKGROUND DATA: Widespread implementation of electronic health...