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chinesehumorsentiment icon chinesehumorsentiment

ChineseHumorSentiment, chinese humor sentiment mining including corpus build and mining nlp methods.中文文本幽默情绪计算项目,项目包括幽默文本语料库的构建,幽默计算模型,包括幽默等级识别,幽默类型识别,隐喻类型识别,隐喻情绪识别等,语言是幽默的,NLP也应该是幽默的.

dingdian icon dingdian

http://cuiqingcai.com/3472.html 这个教程的源码

dpcnn-textcnn-pytorch-inception icon dpcnn-textcnn-pytorch-inception

本项目主要为针对DPCNN(Deep Pyramid Convolutional Neural Networks for Text Categorization )文本分类(Text Classification)的论文复现以及基于知乎看山杯Inception的修改和复现,后者效果略优。

help- icon help-

四川大学大三董凯宁期末作业

keras-textclassification icon keras-textclassification

中文长文本分类、短句子分类、多标签分类、两句子相似度(Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short),字词句向量嵌入层(embeddings)和网络层(graph)构建基类,FastText,TextCNN,CharCNN,TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN

links icon links

A repo that contains outgoing links from DBpedia

nlp-tutorial icon nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

python-api-tesing icon python-api-tesing

python中文库-python人工智能大数据自动化接口测试开发。 书籍下载及python库汇总https://china-testing.github.io/

reptile icon reptile

🏀Python 3网络爬虫 猫眼 腾讯视频 豆瓣 研招网 微博 小说 百度热点 CSDN

scrapyproject icon scrapyproject

开始Scrapy实战如:存数据库、下载文件、爬京东、淘宝、Anti-Anti-Spider……

tensor2tensor icon tensor2tensor

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

text-classification-surveys icon text-classification-surveys

文本分类资源汇总,包括深度学习文本分类模型,如SpanBERT、ALBERT、RoBerta、Xlnet、MT-DNN、BERT、TextGCN、MGAN、TextCapsule、SGNN、SGM、LEAM、ULMFiT、DGCNN、ELMo、RAM、DeepMoji、IAN、DPCNN、TopicRNN、LSTMN 、Multi-Task、HAN、CharCNN、Tree-LSTM、DAN、TextRCNN、Paragraph-Vec、TextCNN、DCNN、RNTN、MV-RNN、RAE等,浅层学习模型,如LightGBM 、SVM、XGboost、Random Forest、C4.5、CART、KNN、NB、HMM等。介绍文本分类数据集,如MR、SST、MPQA、IMDB、Yelp、20NG、AG、R8、DBpedia、Ohsumed、SQuAD、SNLI、MNLI、MSRP、MRDA、RCV1、AAPD,评价指标,如accuracy、Precision、Recall、F1、EM、MRR、HL、Micro-F1、Macro-F1、P@K,和技术挑战,包括多标签文本分类。

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