【论文阅读】Distributed Representation of Sentence and Document
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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相关文章
- 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