Results 11 to 20 of about 24,148,830 (341)

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing [PDF]

ACM Computing Surveys, 2021
This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x),
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig   +5 more
semanticscholar   +1 more source

Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [PDF]

Conference on Empirical Methods in Natural Language Processing, 2023
Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data.
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, Diyi Yang   +5 more
semanticscholar   +1 more source

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey [PDF]

ACM Computing Surveys, 2021
Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, Dan Roth   +8 more
semanticscholar   +1 more source

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [PDF]

ACM Trans. Comput. Heal., 2020
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web.
Yu Gu, Robert Tinn, Hao Cheng, Michael R. Lucas, N. Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon   +8 more
semanticscholar   +1 more source

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing [PDF]

European Conference on Computer Vision, 2022
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research.
Benedikt Boecking, N. Usuyama, Shruthi Bannur, Daniel Coelho de Castro, A. Schwaighofer, Stephanie L. Hyland, M. Wetscherek, Tristan Naumann, A. Nori, Javier Alvarez-Valle, Hoifung Poon, O. Oktay   +11 more
semanticscholar   +1 more source

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages [PDF]

Annual Meeting of the Association for Computational Linguistics, 2020
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization ...
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, Christopher D. Manning   +4 more
semanticscholar   +1 more source

PyThaiNLP: Thai Natural Language Processing in Python [PDF]

NLPOSS, 2023
We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language.
Wannaphong Phatthiyaphaibun, Korakot Chaovavanich, Charin Polpanumas, Arthit Suriyawongkul, Lalita Lowphansirikul, Pattarawat Chormai, Peerat Limkonchotiwat, Thanathip Suntorntip, Can Udomcharoenchaikit   +8 more
semanticscholar   +1 more source

Datasets: A Community Library for Natural Language Processing [PDF]

Conference on Empirical Methods in Natural Language Processing, 2021
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, A. Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, J. Plu, Lewis Tunstall, Joe Davison, Mario vSavsko, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, N. Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, C. Delangue, Th'eo Matussiere, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander M. Rush, Thomas Wolf   +31 more
semanticscholar   +1 more source

Data Augmentation Approaches in Natural Language Processing: A Survey [PDF]

AI Open, 2021
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks ...
Bohan Li, Yutai Hou, Wanxiang Che
semanticscholar   +1 more source

Multimodal Classification of Safety-Report Observations

Applied Sciences, 2022
Modern businesses are obligated to conform to regulations to prevent physical injuries and ill health for anyone present on a site under their responsibility, such as customers, employees and visitors.
Georgios Paraskevopoulos, Petros Pistofidis, Georgios Banoutsos, Efthymios Georgiou, Vassilis Katsouros   +4 more
doaj   +1 more source