统计语言模型&word2vec

深藏阁楼爱情的钟 2022-08-23 03:56 189阅读 0赞

# [我们是这样理解语言的][Link 1] #

[深度学习：语言模型的评估标准][Link 2]

[\[我们是这样理解语言的-2\]统计语言模型][-2]

[自然语言处理 - 语言模型（Language Modeling）][- _Language Modeling]

[NLP 学习笔记 01][NLP _ 01]

[经典统计语言模型][Link 3]

[Statistical language model 统计语言模型][Statistical language model]

[语言模型][Link 4]

## n-gram    ##

[n-gram语言模型][n-gram]

[NLP系列(5)\_从朴素贝叶斯到N-gram语言模型][NLP_5_N-gram]

[二元语法（2-gram）分词中的平滑算法][2-gram]

[第一章 语言模型][Link 5]

[n-gram重探 Q&A][n-gram_ Q_A]

http://www.cnblogs.com/rocketfan/category/284073.html

## n-pos    ##

## 神经网络语言模型ffnnlm    ##

博士论文《基于神经网络的词和文档语义向量表示方法研究》

[Feedforward Neural Network Language Model(NNLM)原理及数学推导 ][Feedforward Neural Network Language Model_NNLM]

[词窗口分类和神经网络][Link 6]

[Recurrent Neural Network Based Language Model(RNNLM)原理及BPTT数学推导][Recurrent Neural Network Based Language Model_RNNLM_BPTT]

## continuous bag of words（cbow）&& skip-gram    ##

[CS 224D: Deep Learning for NLP - LectureNotes1.pdf][CS 224D_ Deep Learning for NLP - LectureNotes1.pdf]

深入 cbow+hierarchical softmax

下面这个讲得很好

[word2vec 中的数学原理详解][word2vec]

## rnnlm ##

[Recurrent Neural Network Based Language Model(RNNLM)原理及BPTT数学推导][Recurrent Neural Network Based Language Model_RNNLM_BPTT]

[ptb\_reader源码解析][ptb_reader]

[tensorflow0.10.0 ptb\_word\_lm.py 源码解析][tensorflow0.10.0 ptb_word_lm.py]

## word2vec    ##

[Deep Learning in NLP （一）词向量和语言模型][Deep Learning in NLP]

[深度学习（Deep Learning），自然语言处理（NLP）及其表达（Representation）][Deep Learning_NLP_Representation]

[深度学习word2vec笔记之基础篇][word2vec 1]

[深度学习word2vec笔记之算法篇][word2vec 2]

[再谈word2vec - 无知的我 - 博客频道 - CSDN.NET][word2vec - _ - _ - CSDN.NET]

[文本深度表示模型—word2vec&doc2vec词向量模型][word2vec_doc2vec]

[word2vec——高效word特征求取][word2vec_word]

# [word2vec原理推导与代码分析][word2vec 3] #

[深度学习（四十二）word2vec词向量学习笔记][word2vec 4]

[word2vec原理概述][word2vec 5]

[文本深度表示模型—word2vec&doc2vec词向量模型][word2vec_doc2vec]

[技术干货 | 漫谈Word2vec之skip-gram模型][_ _Word2vec_skip-gram]

[tensorflow 0.10 word2vec 源码解析][tensorflow 0.10 word2vec]

[word2vec阅读笔记][word2vec 6]

[衡量文档相似性的一种方法-----词移距离 Word Mover's Distance][-----_ Word Mover_s Distance]

[卷积神经网络(CNN)在句子建模上的应用][CNN]

[语义分析的一些方法][Link 7]

[Deep Learning及NLP(自然语言处理)杂谈][Deep Learning_NLP]

## [神经网络语言模型详解][Link 8] ##

[深度学习之六，基于RNN(GRU,LSTM)的语言模型分析与theano代码实现][RNN_GRU_LSTM_theano]

https://github.com/wabyking/Gated\_CNN\_for\_language\_Modeling

[Link 1]: http://www.flickering.cn/ads/2014/06/%E6%88%91%E4%BB%AC%E6%98%AF%E8%BF%99%E6%A0%B7%E7%90%86%E8%A7%A3%E8%AF%AD%E8%A8%80%E7%9A%84-1%E6%96%87%E6%9C%AC%E5%88%86%E6%9E%90%E5%B9%B3%E5%8F%B0textminer/
[Link 2]: http://blog.csdn.net/pipisorry/article/details/78677580
[-2]: http://http//www.flickering.cn/nlp/2015/02/%E6%88%91%E4%BB%AC%E6%98%AF%E8%BF%99%E6%A0%B7%E7%90%86%E8%A7%A3%E8%AF%AD%E8%A8%80%E7%9A%84-2%E7%BB%9F%E8%AE%A1%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/
[- _Language Modeling]: http://blog.csdn.net/lanxu_yy/article/details/29918015
[NLP _ 01]: http://blog.csdn.net/dark_scope/article/details/8616672
[Link 3]: http://blog.csdn.net/abcjennifer/article/details/45419591
[Statistical language model]: http://blog.csdn.net/a635661820/article/details/43906731
[Link 4]: http://wenku.baidu.com/view/a9b2a8e081c758f5f61f6774.html?re=view
[n-gram]: http://http//wenku.baidu.com/view/c1f1460cba1aa8114431d920.html
[NLP_5_N-gram]: http://blog.csdn.net/longxinchen_ml/article/details/50646528
[2-gram]: http://licstar.net/archives/91
[Link 5]: http://www.cnblogs.com/treyscience/p/3746038.html
[n-gram_ Q_A]: http://blog.csdn.net/u011274209/article/details/51500602
[Feedforward Neural Network Language Model_NNLM]: http://blog.csdn.net/a635661820/article/details/44130285
[Link 6]: http://blog.csdn.net/garfielder007/article/details/51001483
[Recurrent Neural Network Based Language Model_RNNLM_BPTT]: http://blog.csdn.net/a635661820/article/details/44462315
[CS 224D_ Deep Learning for NLP - LectureNotes1.pdf]: http://cs224d.stanford.edu/lecture_notes/LectureNotes1.pdf
[word2vec]: http://blog.csdn.net/itplus/article/details/37969519
[ptb_reader]: http://blog.csdn.net/u012436149/article/details/52828782
[tensorflow0.10.0 ptb_word_lm.py]: http://blog.csdn.net/u012436149/article/details/52828786
[Deep Learning in NLP]: http://licstar.net/archives/328
[Deep Learning_NLP_Representation]: http://blog.csdn.net/ycheng_sjtu/article/details/48520293
[word2vec 1]: http://blog.csdn.net/mytestmy/article/details/26961315
[word2vec 2]: http://blog.csdn.net/mytestmy/article/details/26969149
[word2vec - _ - _ - CSDN.NET]: http://blog.csdn.net/jj12345jj198999/article/details/27352659
[word2vec_doc2vec]: http://www.dataguru.cn/article-9478-1.html
[word2vec_word]: http://blog.csdn.net/abcjennifer/article/details/46397829
[word2vec 3]: http://www.hankcs.com/nlp/word2vec.html
[word2vec 4]: http://blog.csdn.net/hjimce/article/details/51564783
[word2vec 5]: http://blog.sina.com.cn/s/blog_66a6172c0102v1k9.html
[_ _Word2vec_skip-gram]: https://mp.weixin.qq.com/s?__biz=MzA5NzY0MDg1NA==&mid=2706953858&idx=1&sn=cb83cb61330cdb77275e846bec6e7433&scene=21#wechat_redirect
[tensorflow 0.10 word2vec]: http://blog.csdn.net/u012436149/article/details/52848013
[word2vec 6]: http://blog.csdn.net/kevinew/article/details/11585945
[-----_ Word Mover_s Distance]: http://blog.csdn.net/qrlhl/article/details/78512598
[CNN]: http://www.jeyzhang.com/cnn-apply-on-modelling-sentence.html
[Link 7]: http://dataunion.org/10748.html
[Deep Learning_NLP]: http://blog.csdn.net/neighborhoodguo/article/details/47909813
[Link 8]: http://mp.weixin.qq.com/s?__biz=MjM5ODIzNDQ3Mw==&mid=203735609&idx=1&sn=68f6e2ad560257869aeea09c59af1c1a&scene=2&from=timeline&isappinstalled=0&utm_source=open-open
[RNN_GRU_LSTM_theano]: http://blog.csdn.net/u010223750/article/details/51510897