LDA(Latent Dirichlet Allocation)是一种经典的主题模型,广泛应用于文本挖掘任务 中。然而,LDA 只适用于长文本,对于短文本分类任务中的词语稀疏和上下文信息丌 足问题没有很好的解决方法。 3. Sentence-LDA 主题模型 为了解决短文本分类任务中的问题,本文提出了一种基于Sentence-LDA 主题模型的短 文本分类方法。该...
以及一些机器学习方法比如潜在语义分析 (Latent Semantic Analysis LSA)，Latent Dirichlet Allocation (LDA)...
统计机器学习时代:词袋模型(one-hot编码)到TF-IDF(稀疏向量),再到word-embedding(密集向量),以及一些机器学习方法比如潜在语义分析 (Latent Semantic Analysis LSA),Latent Dirichlet Allocation (LDA)等 深度学习时代:早期的Word2Vec, GloVe,FastText将词语映射至较低维度的向量空间;BERT等预训练语言模型的出现则促使...
multi-document summarizingsentence scor inglatent dirichlet allocationIn this paper automatic multi-document summarizing in a greedy framework is studied, where sentences are selected based on their contribution for the theme construction of the summary. The scores of sentences are evaluated b...
该主题模型是隐含狄利克雷分布模型(Latent Dirichlet Allocation, LDA)的扩展,假设一个句子只产生一个主题分布。 利用训练好的 Sentence-LDA 主题模型预测原始短文本的主题分布,从而将得到的主题词扩展到原始短文本特征中,完成短文本特征扩展。 对扩展后的短文本使用支持向量机(Support Vector Machine, SVM)进行最后的...
The second approach is considering topic relevance with Latent Dirichlet Allocation. A paragraph which contains words related to article's main topic is more important than other paragraphs including less relevant words. We emphasize paragraphs which highly related to the main topics. The third ...
Convolutional Neural Networks for Sentence
关键词: latent dirichlet allocation text summarization topic models DOI: 10.1109/DEXA.2010.33 被引量: 3 年份: 2010 收藏 引用 批量引用 报错 分享 全部来源 免费下载 求助全文 IEEEXplore (全网免费下载) IEEEXplore Semantic Scholar Semantic Scholar (全网免费下载) 掌桥科研 查看更多 ...
The algorithm proposed algorithm in this paper is able to achieve the score greater than that of text summarization using Latent Dirichlet Allocation (LDA) topic modelling.会议论文Hritvik GuptaMayank Patel
Single Document Keyphrase Extraction Using Sentence Clustering and Latent Dirichlet Allocation This paper describes the design of a system for extracting keyphrases from a single document The principle of the algorithm is to cluster sentences of the documents in order to highlight parts of text that ...