【论文阅读】Distributed Representation of Sentence and Document

时间 2020-12-30

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Abstract 传统的单词模型（bag-of-words）在实际应用中有一些缺点： They lose the ordering of the words，丢失了单词的顺序。 They ignore semantics of the words，忽略了单词的语义。因此本文提出了一种新的模型（Paragraph Vector），解决了上述的一些问题，带来了更好的效果。 Introduction

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1. Distributed Representations of Words and Phrases and their Compositionality论文阅读及实战
2. 【论文笔记】A model of coherence based on Distributed Sentence Representation
3. iCaRL：Incremental Classifier and Representation Learning论文阅读
4. 《Same Representation,Different Attentions:Shareable Sentence Representation》阅读笔记
5. [NLP论文阅读] Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning
6. Sentence-BERT 论文阅读
7. [NLP论文阅读] Supervised Learning of Universal Sentence Representations from NLI data
8. Distributed Representations of Sentences and Documents阅读笔记
9. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
10. Inductive Representation Learning on Large Graphs 论文阅读

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