2.基于Rnn的模型把text看作单词序列并且捕获单词的独立性和text的结构(RNN-based models view text as a sequence of words,and are intended to capture word dependencies and text structure) 3.基于CNN的模型训练识别text的模式,像是关键短语在文本分类任务上(CNN-based models are trained to recognize patterns...
We evaluate CNN, LSTM, ULMFiT, and BERT based models on two publicly available Marathi text classification datasets and present a comparative analysis. The pre-trained Marathi fast text word embeddings by Facebook and IndicNLP are used in conjunction with word-based models. We show that basic ...
《Deep learning Based Text Classification:A comprehensive Review》文本分类综述 xtdx 20 人赞同了该文章 1.介绍 深度学习综述年年有,今年特别多。随着深度学习在机器学习领域的快速发展,对每个任务进行算法的总结对于之后的发展是有益的。综述可以梳理发展脉络,对比算法好坏,并为以后的研究方向进行启发。本文是在NLP...
Text Classification 基于Keras的15种模型:TextCNN, TextRNN, TextDPCNN, TextRCNN, TextHAN, TextBert等及其变种 支持5类特征及其组合:word-level, char-level, 结构化特征(TFIDF, LSA), Context特征(word-left, word-right, char-left, char-right), sentence-level 支持4种分类任务:单标签二分类,单标签多...
A unique characteristic of our proposed work, when compared to existing ones, is that it does not require a pre-processing phase and fully based on deep learning models. Besides, we studied the impact of utilizing word2vec embedding models to improve the performance of the classification tasks....
In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers' models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods ...
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this work, we provide a detailed review of more than 150 deep learning based...
Text has been split into one sentence per line. The data has been used for a few related natural language processing tasks. For classification, the performance of machine learning models (such as Support Vector Machines) on the data is in the range of high 70% to low 80% (e.g. 78%-...
Different depth learning models can be formed according to different feature learning and its combination. However, the accuracy of image classification is not high and the operation efficiency of the existing deep learning model is low. Therefore, based on the existing basic theory of convolution ...
我们的目的是将无序函数的速度和语法函数的准确性相结合,本节首先介绍一种无序合成函数“neural bag-of-words models”(NBOW),避免探索太复杂的语法函数,防止和NBOW模型相关的许多陷阱。最后展示了deep averaging network( DAN ),其在传统NBOW模型上堆叠多个非线性层,并取得和语法函数相当甚至更好的性能。 1)Neu...