Aspect Categorization Using Domain-Trained Word Embedding and Topic Modelling

机译：使用领域训练词嵌入和主题建模的方面分类

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来源
《International conference on computer science, electronics and engineering》|2019年|191-198|共8页
会议地点 Kuala Lumpur(MA)
作者
Omar Mustafa Al-Janabi; Nurul Hashimah Ahamed Hassain Malim; Yu-N Cheah;
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作者单位

School of Computer Science Universiti Sains Malaysia Gelugor Malaysia;

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会议组织
原文格式 PDF
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关键词
Aspect-based; Aspect category; Word-to-topic distribution; LDA; Word embedding;

机译：基于方面;方面类别;单词到主题的分布; LDA;词嵌入;

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1. Relational Biterm Topic Model: Short-Text Topic Modeling using Word Embeddings [J] . Li Ximing, Zhang Ang, Li Changchun, The Computer journal . 2019,第3期

机译：关系双项主题模型：使用词嵌入的短文本主题建模
2. Adaptive cross-contextual word embedding for word polysemy with unsupervised topic modeling [J] . Li Shuangyin, Pan Rong, Luo Haoyu, Knowledge-Based Systems . 2021,第Apra22期

机译：与无监督主题建模的自适应交叉上下文词嵌入Word Polysemy
3. A HYBRID WORD EMBEDDING MODEL BASED ON ADMIXTURE OF POISSON-GAMMA LATENT DIRICHLET ALLOCATION MODEL AND DISTRIBUTED WORD-DOCUMENT-TOPIC REPRESENTATION [J] . IBRAHIM BAKARI BALA, MOHD ZAINURI SARINGAT, AIDA MUSTAPHA Journal of Theoretical and Applied Information Technology . 2020,第9期

机译：一种基于泊松 - 伽马潜在Dirichlet分配模型和分布式字文档主题表示的混合词嵌入模型
4. Aspect Categorization Using Domain-Trained Word Embedding and Topic Modelling [C] . Omar Mustafa Al-Janabi, Nurul Hashimah Ahamed Hassain Malim, Yu-N Cheah International conference on computer science, electronics and engineering . 2020

机译：使用域训练的单词嵌入和主题建模的方面分类
5. Things and Strings and More: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence, Topic Modeling, and Word Embedding [D] . Ju, Yiting. 2017

机译：事物和字符串和更多：通过组合实体共同发生，主题建模和单词嵌入来改善从短文本的歧义
6. Nonparametric Spherical Topic Modeling with Word Embeddings [O] . Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, -1

机译：带词嵌入的非参数球形主题建模
7. A word embedding topic model for topic detection and summary in social networks [O] . Lei Shi, Gang Cheng, Shang-ru Xie, 2019

机译：社交网络主题检测和摘要的单词嵌入主题模型

Aspect Categorization Using Domain-Trained Word Embedding and Topic Modelling

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