1.提出了一种基于特征函数和反向转录文法(ITG)的无监督词对齐模型,使用对数线性模型对文法规则的概率建模,先验知识可以通过特征函数的形式加入到模型里面,而模型仍然可以进行无监督训练.2. 在模型的参数训练方面,本文在模型的优化目标上增加了一个L1正则化因子,使得模型能学到一个稀疏的解,把文法规则概率集中到了对词对齐有用的文法规则上面,提高了词对齐的质量.3. 开发了一个基于ITG的无监督词对齐软件工具,实现了传统的ITG无监督词对齐模型和基于特征函数的ITG无监督词对齐模型. 本文在词对齐和机…

论文名和编号 摘要/引言 相关背景和工作 论文方法/模型 实验(数据集)及 分析(一些具体数据) 未来工作/不足 是否有源码 问题 原因 解决思路 优势 基于表示学习的中文分词 编号:1001-9081(2016)10-2794-05 1.为提高中文分词的准确率和未登录词识别率. 1.分词后计算机才能得知中文词语的确切边界,进而理解文本中所包含的语义信息.中文分词是中文自然语言处理的一项基础性工作,是中文信息处理技术发展的技术瓶颈. 1.使用skip-gram模型将文本中的词映射为高维向量空间中…

Speech and Natural Language Processing obtain from this link: https://github.com/edobashira/speech-language-processing A curated list of speech and natural language processing resources. Other lists can be found in this list. If you want to contribut…

翻译 Improved Word Representation Learning with Sememes 题目 Improved Word Representation Learning with Sememes 融合义原知识的词汇表示学习 摘要 Abstract Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by sev…

网上有一些基础的东西,但是比如插入图片,就没有找到方案,最终自己摸索出来的. 1.首先通过Nuget获取引用,关键字:“DocX” 2.示例代码 class Program { static void Main(string[] args) { string path = @"C:\Users\Administrator\Desktop\test.docx"; using (var document = DocX.Create(path)) { //文字居中对齐 document.In…

The present invention relates to an apparatus for supporting information centric networking. An information centric network (ICN) node based on a switch according to the present invention includes an ICN process configured to request information for…

  https://support.office.com/en-us/article/Word-keyboard-shortcuts-c0ca851f-3d58-4ce0-9867-799df73666a7     To the beginning of a document + HOME + FN + LEFT ARROW (on a MacBook keyboard)   opt + command + 数字,更改样式  Go to the previous field. This keyb…

  Sheryl prefers passive voice for some of her writing (such as business documents and correspondence) rather than active voice. The grammar checker on Word always marks instances of passive voice. Sheryl would like to turn off the portion of the gra…

This article come from HEREARS-L1: Learning Tuesday 10:30–12:30; Oral Session; Room: Leonard de Vinci 10:30  ARS-L1.1—GROUP STRUCTURED DIRTY DICTIONARY LEARNING FOR CLASSIFICATION Yuanming Suo, Minh Dao, Trac Tran, Johns Hopkins University, USA; Hojj…

awesome-text-summarization 2018-07-19 10:45:13 A curated list of resources dedicated to text summarization Contents Corpus Opinosis dataset contains 51 articles. Each article is about a product’s feature, like iPod’s Battery Life, etc. and is a colle…

ICLR 2014 International Conference on Learning Representations Apr 14 - 16, 2014, Banff, Canada Workshop Track Submitted Papers Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence Mathias Berglund, Ta…

@http://www-cs-faculty.stanford.edu/people/karpathy/cvpr2015papers/ CVPR 2015 papers (in nicer format than this) maintained by @karpathy NEW: This year I also embedded the (1,2-gram) tfidf vectors of all papers with t-sne and placed them in an interf…

From:  http://www.pamitc.org/cvpr15/program.php Official Program for CVPR 2015 Monday, June 8 8:30am-8:40am Ballrooms A,B,C Rooms 302,304,306 Opening Remarks from Conference Chairs The opening remarks will be made from Ballrooms A,B,C, but a live vid…

转自:https://github.com/andrewt3000/DL4NLP Deep Learning for NLP resources State of the art resources for NLP sequence modeling tasks such as machine translation, image captioning, and dialog. My notes on neural networks, rnn, lstm Deep Learning for NL…

Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection 动态池和展开递归自动编码器的意译检测 论文地址 Richard Socher,Eric H. Huang, Jeffrey Pennington∗ , Andrew Y. Ng, Christopher D. Manning Computer Science Department, Stanford University, Stanford,…

100篇必读的NLP论文 100 Must-Read NLP 自己汇总的论文集,已更新 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 probabl…

A Summary of Multi-task Learning author by Yubo Feng. Intro In this paper[0], the introduction of multi-task learning through the data hungry, the most common problem of Deep Learning[1]. Basic assumption: tasks are related. MTL mimic human learning…

Natural Language Processing Tasks and Selected References I've been working on several natural language processing tasks for a long time. One day, I felt like drawing a map of the NLP field where I earn a living. I'm sure I'm not the only person who…

Accepted Papers     Title Primary Subject Area ID 3D computer vision 93 UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability 128 Video Registration to SfM Models 168 Image-based 4-d Modeling Using 3-d Change Detect…

CVPR2015 Papers震撼来袭! CVPR 2015的文章可以下载了,如果链接无法下载,可以在Google上通过搜索paper名字下载(友情提示:可以使用filetype:pdf命令). Going Deeper With ConvolutionsChristian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke…

http://blog.csdn.net/garfielder007/article/details/51480525 New (draft) survey paper: Labeled Faces in the Wild: A SurveyErik Learned-Miller, Gary Huang, Aruni RoyChowdhury, Haoxiang Li, Gang Hua The camera-ready has not yet been submitted. If you se…

记录下,有空研究. http://nlp.stanford.edu/projects/DeepLearningInNaturalLanguageProcessing.shtml http://nlp.stanford.edu/courses/NAACL2013/ Fast and Robust Neural Network Joint Models for Statistical Machine Translation ACL2014的论文列表 http://blog.sina.com.cn/s…

GitHub NLP项目:自然语言处理项目的相关干货整理 自然语言处理(NLP)是计算机科学,人工智能,语言学关注计算机和人类(自然)语言之间的相互作用的领域.本文作者为自然语言处理NLP初学者整理了一份庞大的自然语言处理项目领域的概览,包括了很多人工智能应用程序.选取的参考文献与资料都侧重于最新的深度学习研究成果.这些自然语言处理项目资源能为想要深入钻研一个自然语言处理NLP任务的人们提供一个良好的开端. 自然语言处理项目的相关干货整理: 指代消解 https://github.com/Kyu…

Page 1Published as a conference paper at ICLR 2017AS IMPLE BUT T OUGH - TO -B EAT B ASELINE FOR S EN -TENCE E MBEDDINGSSanjeev Arora, Yingyu Liang, Tengyu MaPrinceton University{arora,yingyul,tengyu}@cs.princeton.eduA BSTRACTThe success of neural net…

软件需求: 首先你必须要有Moses(废话哈哈).然后要有GIZA++用作词对齐(traning-model.perl的时候会用到).IRSTLM产生语言模型 大致步骤: 大体的步骤如下: 准备Parallerl data(需要句子对齐):对语料进行tokenisation.truecasing和cleaning步骤之后才能使用于我们的机器翻译系统(哈哈,都快忍不住直接写详细步骤了) 训练你的语言模型(使用IRSTLM):当然也有几步,详细叙述再说 然后就是训练你的翻译系统啦(可能要花一两个小时…

http://www.cnblogs.com/cenalulu/p/3587006.html   背景:最近采购了一批新的服务器,底层的存储设备的默认physical sector size从原有的 512B 改为了 4K. 装完系统以后,在做数据库物理备份恢复时xtrabackup报了这么一个错.但是同样的备份在512B sector size的老系统上却可以恢复. 报错如下: InnoDB: Error: tried to read 2048 bytes at offset 0 0.Inno…

ref:http://www.coranac.com/tonc/text/asm.htm 23.1. Introduction Very broadly speaking, you can divide programming languages into 4 classes. At the lowest level is machine code: raw numbers that the CPU decodes into instructions to execute. One step u…

Recommender Systems with Deep Learning Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers – Authors: C Verma, M Hart, S Bhatkar, A Parker (2016) Multi-modal learning for video recommendation based on mobile…

Recommender Systems with Deep Learning Alessandro:ADAAlessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2…

Spark工作机制以及API详解 本篇文章将会承接上篇关于如何部署Spark分布式集群的博客,会先对RDD编程中常见的API进行一个整理,接着再结合源代码以及注释详细地解读spark的作业提交流程,调度机制以及shuffle的过程,废话不多说,我们直接开始吧! 1. Spark基本API解读 首先我们写一段简单的进行单词统计的代码,考察其中出现的API,然后做出整理: import org.apache.spark.SparkConf; import org.apache.spark.api.j…