Efficient Methods for Natural Language Processing: A Survey

Tim Schopf F. Matthes Karim Arabi

20 Juli 2023

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, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.

Jan Sawicki M. Ganzha M. Paprzycki

3 Juli 2023

ABSTRACT Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current “state of the field” and thus, to enter it. This brought the id-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

J. Kalita Julian R. Medina Dan Otter

21 April 2020

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 article 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 studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to many applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.

Xuanjing Huang Xipeng Qiu Ning Dai + 3 lainnya

18 Maret 2020

Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next, we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

Lun-Wei Ku Cheng-te Li

30 April 2023

SocialNLP is an inter-disciplinary area of natural language processing (NLP) and social computing. SocialNLP has three directions: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing. The 11th SocialNLP workshop is held at TheWebConf 2023. We accepted nine papers with acceptance ratio 56%. We sincerely thank to all authors, program committee members, and workshop chairs, for their great contributions and help in this edition of SocialNLP workshop.