【論文閱讀】Distributed Representation of Sentence and Document
Abstract 傳統的單詞模型（bag-of-words）在實際應用中有一些缺點： They lose the ordering of the words，丟失了單詞的順序。 They ignore semantics of the words，忽略了單詞的語義。 因此本文提出了一種新的模型（Paragraph Vector），解決了上述的一些問題，帶來了更好的效果。 Introduction
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相關文章
- 1. Distributed Representations of Words and Phrases and their Compositionality論文閱讀及實戰
- 2. 【論文筆記】A model of coherence based on Distributed Sentence Representation
- 3. iCaRL：Incremental Classifier and Representation Learning論文閱讀
- 4. 《Same Representation,Different Attentions:Shareable Sentence Representation》閱讀筆記
- 5. [NLP論文閱讀] Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning
- 6. Sentence-BERT 論文閱讀
- 7. [NLP論文閱讀] Supervised Learning of Universal Sentence Representations from NLI data
- 8. Distributed Representations of Sentences and Documents閱讀筆記
- 9. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 10. Inductive Representation Learning on Large Graphs 論文閱讀