latest research papers on natural language processing

Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. ... Natural Language Processing. 2335 benchmarks • 663 tasks • 2017 datasets • 27934 papers with code Representation Learning Representation Learning. 16 benchmarks 3663 papers with code Disentanglement. 3 benchmarks ...

Natural language processing (NLP) has recently gained much attention for representing and analyzing 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. In this paper, we first distinguish four phases by discussing different levels of NLP ...

Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches. We also present approaches that use pre-trained language models to generate data for ...

Abstract. The field of Natural Language Processing (NLP) has evolved with, and as well as influenced, recent advances in Artificial Intelligence (AI) and computing technologies, opening up new applications and novel interactions with humans. Modern NLP involves machines' interaction with human languages for the study of patterns and obtaining ...

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require ...

Over the last decade, deep learning has revolutionized machine learning. Neural network architectures have become the method of choice for many different applications; in this paper, we survey the applications of deep learning to natural language processing (NLP) problems. We begin by briefly reviewing the basic notions and major architectures of deep learning, including some recent advances ...

This special issue presents opinion articles, opinion-oriented reviews as well as original research papers on natural reading and speech processing. They propose new theoretical approaches and frameworks for language processing under natural conditions (Falandays et al., Citation 2018; Hadley & Pickering, Citation 2018; Hamilton & Huth ...

Fig. 2. A high-level overview of the various research efforts in the domain of robustness analysis across various elements of the NLP pipeline, including techniques, embedding, metrics, benchmarks, attack model, and defense mechanisms. description, we envision the application of a natural language model (used for natural language generation).

learning in current neural network architectures that mirrors classical linguistic structures (Hewitt and 14 Manning, 2019;Tenney et al., 2019).In terms of developing systems endowed with natural language capabilities, the last generation of neural 16 network architectures have allowed AI and NLP to make unprecedented progress. Such systems (e.g., the 17 GPT family) are typically trained with ...

The Open Access Natural Language Processing Journal aims to advance modern understanding and practice of trustworthy, interpretable, explainable human-centered and hybrid Artificial Intelligence as it relates to all aspects of human language. The NLP journal affords a world-wide platform for academics and practitioners to present their latest ...

In this research paper, a comprehensive literature review was undertaken in order to analyze Natural Language Processing (NLP) application based in different domains. Also, by conducting qualitative research, we will try to analyze the development of the current state and the challenge of NLP technology as a key for Artificial Intelligence (AI ...

Staying in natural language understanding tasks, Question and Answering (Q & A) systems still emerge as a continuous topic of research. In this regard, the paper by proposes an attention model to solve question difficulty estimation in Question-Answering tasks. The method first relates question and information components using dual multi-head ...

Dhiraj Jadhav. Department of Data Science &. Artificial Intelligence. Bournemouth University. [email protected]. Abstract — This research paper explores recent. advancements in Natural ...

Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more. Our work spans the range of traditional NLP tasks, with general-purpose syntax and ...

Hindi Language. Image captioning refers to the process of generating a textual description that describes objects and activities present in a given image. It connects two fields of artificial intelligence, computer vision, and natural language processing. Computer vision and natural language processing deal with image understanding and language ...

As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics ...

At Cohere, we're excited about natural language processing and all the amazing accomplishments it has made in recent years. Staying up to date with the latest research can be challenging, though, as new papers come out every month. That's why we put together this list of some of the best papers on NLP for 2022—so you don't have to miss a thing!

Performing groundbreaking Natural Language Processing research since 1999.

We show in Fig. 2 the number of publications retrieved and the methods used in our review, reflecting the trends of the past 10 years. We can observe that: (1) there is an upward trend in NLP ...

Natural language processing (NLP) has recently gained much attention for representing and analyzing 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. In this paper, we first distinguish ...

time to learn new languages or get perfection in it. A language can be defined as a set of rules or set of symbol. Symbol are combined and used for conveying information or broadcasting the information. Symbols are tyrannized by the Rules. Natural Language Processing basically can be classified into two parts i.e. Natural

The normal language preparing communicates its interest through a tremendous wide range of utilizations. Already, NLP used to manage static data. Nowadays, NLP is doing impressively with the corpus, lexicon database, and pattern reorganization. These incorporate framework of communicated in language that coordinate discourse and regular language.

Abstract. Natural language processing (NLP) has r ecently gained much attention for representing and. analysing human language computational ly. It has spread its applications in various fields ...

This paper describes experiments showing that some tasks in natural language processing (NLP) can already be performed using quantum computers, though so far only with small datasets. We demonstrate various approaches to topic classification. The first uses an explicit word-based approach, in which word-topic weights are implemented as fractional rotations of individual qubits, and a phrase is ...

Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial.

Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in ...