25 papers · ranked by Valyu relevance
Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh
Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper…
Roberta Rocca, Nicolò Tamagnone, Selim Fekih, Ximena Contla + 1 more
Natural language processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence, which is concerned with developing methods to process and generate language at scale. Modern NLP tools have the potential to support humanitarian action at multiple stages of…
Georgiana Tucudean, Marian Bucos, Bogdan Dragulescu, Catalin Daniel Caleanu + 1 more
'Catalin Daniel Caleanu' 'Maria Navarro-Caceres'] Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. This study aims to briefly summarize the use cases for NLP tasks along with the main architectures. This…
Joshua Conrad Jackson, Joseph Watts, Johann-Mattis List, Curtis Puryear + 2 more
'Curtis Puryear' 'Ryan Drabble' 'Kristen A. Lindquist'] Humans have been using language for millennia but have only just begun to scratch the surface of what natural language can reveal about the mind. Here we propose that language offers a unique window into psychology. After briefly summarizing the legacy of language…
Daniel W. Otter, Julian Richard Medina, Jugal Kalita
—Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent…
Jiwei Li, Eduard Hovy
It is commonly accepted that machine translation is a more complex task than part of speech tagging. But how much more complex? In this paper we make an attempt to develop a general framework and methodology for computing the informational and/or processing complexity of NLP applications and tasks. We define a…
Kevin Mote
| | | INTRODUCTION: "WHY CAN'T COMPUTERS UNDERSTAND PLAIN ENGLISH?" 1 | | --- | --- | --- | | 1. | THE PURPOSE: | 3 | | 1.1 | | FOUNDATION: "THE HUMAN BRAIN AND COGNITIVE LINGUISTICS" 6 | | 1.2 | | METHODOLOGY: " THE GREAT DEBATE: RULES OR STATISTICS " 9 | | | 1.2.1 | The Rationalists: RULES 10 | | | 1.2.2 | The…
Liping Zhao, Waad Alhoshan, Alessio Ferrari, Keletso J. Letsholo
—Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to more recent attempts with machine learning (ML) and deep learning (DL) techniques. However, in spite of the progress, our recent survey…
Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Novanto Yudistira
The rise of big data analytics on top of NLP increasing the computational burden for text processing at scale. The problems faced in NLP are very high dimensional text, so it takes a high computation resource. The MapReduce allows parallelization of large computations and can improve the efficiency of text processing.…
Authors not listed
Natural language processing with the help of large language models such as ChatGPT has become ubiquitous in many software applications and allows users to interact even with complex hardware or software in an intuitive way. The recent concepts of Self-Driving Labs and Material Acceleration Platforms stand to benefit…
Marie P.F. Corradi, Alyanne M. de Haan, Bernard Staumont, Aldert H. Piersma + 4 more
'Aldert H. Piersma' 'Liesbet Geris' 'Raymond H.H. Pieters' 'Cyrille A.M. Krul' 'Marc A.T. Teunis'] Title: Highlights 1. • Natural language processing can support adverse outcome pathways building. 2. • Natural language processing can support new approach methodologies development.
Michael Chary, Saumil Parikh, Alex F. Manini, Edward W. Boyer + 1 more
'Michael Radeos'] Natural language processing (NLP) aims to program machines to interpret human language as humans do. It could quantify aspects of medical education that were previously amenable only to qualitative methods. The application of NLP to medical education has been accelerating over the past several years.…
Upendra Singh, Anant Saraswat, Hiteshwar Kumar Azad, Kumar Abhishek + 1 more
'S Shitharth'] According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews…
Jing Cai, Alex E. Hadjinicolaou, Angelique C. Paulk, Ziv M. Williams + 1 more
Human verbal communication requires a rapid interplay between speech planning, production, and comprehension. These processes are subserved by local and long-range neural dynamics across widely distributed brain areas. How linguistic information is precisely represented during natural conversation or what shared neural…
Reto Gubelmann
Taking Leibniz' ideal of a universal truth-calculating machine as a vantage point, this article provides a philosophically sound analysis of the concept of reasoning in NLP. It argues that reasoning always involves inference, which in turn requires being guided by reason relations. Based on this, the article argues…
Meera M Krishna, Swapnil G Waghmare, Emily C Maccoux, Tania Shaik + 1 more
Aging selectively degrades neuronal structure and function, yet the signals that actively preserve neuronal integrity over adult life remain incompletely defined. In Caenorhabditis elegans, the PVD sensory neuron develops progressive excessive higher-order dendritic branching during normal aging that correlates with…
Sreejan Kumar, Theodore R. Sumers, Takateru Yamakoshi, Ariel Goldstein + 5 more
Humans use complex linguistic structures to transmit ideas to one another. The brain is thought to deploy specialized computations to process these structures. Recently, a new class of artificial neural networks based on the Transformer architecture has revolutionized the field of language modeling, attracting…
Alessandro Lopopolo, Milena Rabovsky
Recent research has shown that the internal dynamics of an artificial neural network model of sentence comprehension displayed a similar pattern to the amplitude of the N400 in several conditions known to modulate this event-related potential. These results led 63 to suggest that the N400 might reflect change in an…
Daniel Mitropolsky, Christos H. Papadimitriou
Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. We introduce a simple mathematical formulation of six basic and broadly accepted principles of…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Dimitris Gkoumas, Maria Liakata
The intersection of chemistry and Artificial Intelligence (AI) is an active area of research focused on accelerating scientific discovery. While using large language models (LLMs) with scientific modalities has shown potential, there are significant challenges to address, such as improving training efficiency and…
Joseph Manning, Lev Sarkisov
With the continuously growing number of scientific articles on synthesis of nanomaterials, it becomes impossible for researchers to grasp and comprehend the landscape of synthetic protocols available for a particular material. The aim of this study is to explore the feasibility of extracting the collective knowledge on…
Esben Bjerrum, Tobias Rastemo, Ross Irwin, Christos Kannas + 1 more
Recent years have seen a large interest in using the Simplified Molecular Input Line Entry System (SMILES) chemical language as input for deep learning architectures solving chemical tasks. Many successful applications have been demonstrated within de novo molecular design, quantitative structure-activity relationship…
Kohulan Rajan, Achim Zielesny, Christoph Steinbeck
Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…