Performance Study of N-grams in the Analysis of Sentiments
Journal of Nigerian Society of Physical Sciences, 2021 In this work, a study investigation was carried out using n-grams to classify sentiments with different machine learning and deep learning methods. We used this approach, which combines existing techniques, with the problem of predicting sequence tags to
O. E. Ojo +3 more
doaj +1 more source
Translating Natural Language to Planning Goals with Large-Language Models [PDF]
arXiv.org, 2023 Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains.
Yaqi Xie +5 more
semanticscholar +1 more source
Design of autonomous family companion robot based on ROS
Dianzi Jishu Yingyong, 2021 Aiming at the problem of left-behind children, empty-nest elderly, and people with reduced mobility, an autonomous family companion robot was designed.
Li Jianyong +3 more
doaj +1 more source
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
M. Lewis +7 more
semanticscholar +1 more source
CLAP Learning Audio Concepts from Natural Language Supervision
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Mainstream machine listening models are trained to learn audio concepts under the paradigm of one class label to many recordings focusing on one task.
Benjamin Elizalde +3 more
semanticscholar +1 more source
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding [PDF]
BlackboxNLP@EMNLP, 2018 Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data.
Alex Wang +5 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 +8 more
semanticscholar +1 more source
Translation between Molecules and Natural Language [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 We present MolT5 - a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.
Carl N. Edwards +4 more
semanticscholar +1 more source
A large annotated corpus for learning natural language inference [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research
Samuel R. Bowman +3 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 +4 more
semanticscholar +1 more source