Academic Articles & Research Papers

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...

NLP (Natural Language Processing) for NLP (Natural Language Programming)

Natural Language Processing

Natural language processing (NLP) is an interdisciplinary subfield, which has components from major fields such as linguistics, computer science, and artificial intelligence. NLP is mainly concerned with the interaction between humans and c...

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...

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...

Automated Encoding of Clinical Documents Based on Natural Language Processing

OBJECTIVE: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method. METHODS: An existing ...

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...

Biomedical Natural Language Processing

Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the per...

Recent Advances in Natural Language Processing

This volume brings together revised versions of a selection of papers presented at the Second International Conference on “Recent Advances in Natural Language Processing” (RANLP’97) held in Tzigov Chark, Bulgaria, September 1997. The aim of...

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...

Detection of Duplicate Defect Reports Using Natural Language Processing

Defect reports are generated from various testing and development activities in software engineering. Sometimes two reports are submitted that describe the same problem, leading to duplicate reports. These reports are mostly written in stru...

Natural language processing (NLP) in management research: A literature review

Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in management research, there is neither a comprehen...

Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition

Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yi...

Integrating NLP to Enhance Algorithmic Identification of Metastatic and Castration‐Resistant Prostate Cancer in Large Claims‐Based Studies

ABSTRACT Purpose Accurate classification of prostate cancer (PC) disease states defined by the presence or absence of metastasis and castration resistance (CRPC) is critical but challenging in population‐based research. As chart review is n...

Sentiment Analysis using the Support Vector Machine Algorithm on Covid_19

This massive development of information technology makes it easier for people's lives in various fields, one of them is social media, social media that people use a lot to get information about news or events that are happening in Indonesia...

Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

Neural networks models for NLP are typically implemented without the explicit encoding of language rules and yet they are able to break one performance record after another. This has generated a lot of research interest in interpreting the ...

What Is Natural Language Processing?

Deep learning and machine learning continues to proliferate throughout various industries, and has revolutionized the topic that I wish to discuss in this book: natural language processing (NLP). NLP is a subfield of computer science that i...

Natural language processing and its future in medicine

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be struct...