100篇必读的NLP论文

原文地址100 Must-Read NLP

自己整理的汇总论文链接：https://pan.baidu.com/s/16k2s2HYfrKHLBS5lxZIkuw 提取码：x7tn

This is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field should probably know about and read.

这是100篇重要的自然语言处理(NLP)论文的列表，认真的学生和研究人员在这个领域应该知道和阅读。This list is compiled by Masato Hagiwara. 本榜单由Masato Hagiwara编制。

I welcome any feedback on this list. 我欢迎对这个列表的任何反馈。 This list is originally based on the answers for a Quora question I posted years ago: What are the most important research papers which all NLP studnets should definitely read?.

这个列表最初是基于我多年前在Quora上发布的一个问题的答案:所有NLP学生都应该阅读的最重要的研究论文是什么?。

I thank all the people who contributed to the original post. 我感谢所有为原创文章做出贡献的人。

This list is far from complete or objective, and is evolving, as important papers are being published year after year.

由于重要的论文年复一年地发表，这份清单还远远不够完整和客观，而且还在不断发展。

Please let me know via pull requests and issues if anything is missing.

请通过pull requests和issues告诉我是否有任何遗漏。

Also, I didn‘t try to include links to original papers since it is a lot of work to keep dead links up to date.

此外，我没有试图包括原始论文的链接，因为保持死链接是大量的工作，直到最新。

I‘m sure you can find most (if not all) of the papers listed here via a single Google search by their titles.

我相信你可以通过一个简单的谷歌搜索找到这里列出的大部分(如果不是全部)论文。

A paper doesn‘t have to be a peer-reviewed conference/journal paper to appear here.

一篇论文不一定要经过同行评审的会议/期刊论文才能出现在这里。

We also include tutorial/survey-style papers and blog posts that are often easier to understand than the original papers.

我们还包括教程/调查风格的论文和博客文章，通常比原来的论文更容易理解。

Clustering & Word Embeddings

集群和词嵌入

• Peter F Brown, et al.: Class-Based n-gram Models of Natural Language, 1992.

  基于类的n-gram自然语言模型

• Tomas Mikolov, et al.: Efficient Estimation of Word Representations in Vector Space, 2013.

  向量空间中字表示的有效估计

• Tomas Mikolov, et al.: Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.

  单词和短语的分布式表示及其组合性

• Quoc V. Le and Tomas Mikolov: Distributed Representations of Sentences and Documents, 2014.

  分布式句子和文档的表示形式

• Jeffrey Pennington, et al.: GloVe: Global Vectors for Word Representation, 2014.

  词表示的全局向量

• Ryan Kiros, et al.: Skip-Thought Vectors, 2015.

  Skip-Thought 向量

• Piotr Bojanowski, et al.: Enriching Word Vectors with Subword Information, 2017.

  用子单词信息丰富单词向量

100篇必读的NLP论文

原文：https://www.cnblogs.com/wwj99/p/12086792.html