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In this paper, we propose a novel representation for text documents based on aggregating word embedding vectors into document embeddings.
The Reuters-21578 text categorization test collection
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Hervé Jégou, Matthijs Douze, Cordelia Schmid, and Patrick Pérez. 2010 · 2010
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Aggregating continuous word embeddings for information retrieval
Stéphane Clinchant and Florent Perronnin. 2013 · 2013
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Kernels for Visual Words Histograms
Radu Tudor Ionescu and Marius Popescu. 2013 · 2013
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Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition
Radu Tudor Ionescu, Marius Popescu, and Cristian Grozea. 2013 · 2013
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Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
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A Unified Model for Word Sense Representation and Disambiguation
Xinxiong Chen, Zhiyuan Liu, and Maosong Sun. 2014 · 2014
Self-Adaptive Hierarchical Sentence Model
Han Zhao, Zhengdong Lu, and Pascal Poupart. 2015 · 2015
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Learning Distributed Representations of Sentences from Unlabelled Data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
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Embeddings for Word Sense Disambiguation: An Evaluation Study
Ignacio Iacobacci, Mohammad Taher Pilehvar, and Roberto Navigli. 2016 · 2016
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From word embeddings to document similarities for improved information retrieval in software engineering
Xin Ye, Hui Shen, Xiao Ma, Răzvan Bunescu, and Chang Liu. 2016 · 2016
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Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
Peng Zhou, Zhenyu Qi, Suncong Zheng, Jiaming Xu, Hongyun Bao, and Bo Xu. 2016 · 2016
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Objectness to improve the bag of visual words model
Radu Tudor Ionescu and Marius Popescu. 2014 · 2014
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Convolutional Neural Networks for Sentence Classification
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Distributed Representations of Sentences and Documents
Quoc Le and Tomas Mikolov. 2014 · 2014
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GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Unsupervised Most Frequent Sense Detection using Word Embeddings
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ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
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Dynamic compositional neural networks over tree structure
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TreeNet: Learning Sentence Representations with Unconstrained Tree Structure
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Bag of meta-words: A novel method to represent document for the sentiment classification
Mingsheng Fu, Hong Qu, Li Huang, and Li Lu. 2018 · 2018
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Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
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A Document Descriptor using Covariance of Word Vectors
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Differentiated attentive representation learning for sentence classification
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