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aeda_nlp

Data augmentation for NLP, accepted at EMNLP 2021 Findings

ai-emotion-detection-web-app

an Emotion Detection system that processes feedback provided by the customer in text format and deciphers the associated emotion expressed.

ccac_2022_task2

面向微博话题的群体情感识别

deberta

The implementation of DeBERTa

deeplearning

dta-topic_model-baseline

Topic Extraction baseline for Dialogue Text Analysis Task of nlpcc 2022

emlm

This is the code for our ACL 2021 paper entitled eMLM: A New Pre-training Objective for Emotion Related Tasks

emotion-detection-with-twitter-post-using-nlp-techniques

In this project, i have developed a system which can detect emotion from text. Besides it can retrive tweet post from twiiter.

fine-grained-emotion-recognition

goemotions-pytorch

Pytorch Implementation of GoEmotions 😍😢😱

google-research

Google Research

lcl_loss

machine-learning-1

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

metalearning-emotion-datasource

In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one.

nlp_emotion_detection_in_text

p-tuning

P-tuning方法在中文上的简单实验

similar-sentence-pairs-in-epidemic

天池大赛疫情文本挑战赛