A recursive statistical translation model

残差的英文单词

Residuals, in the context of statistical analysis, refer

to the differences between observed values and the values

predicted by a model. They are crucial in understanding the

performance of a statistical model and can provide insights

into its accuracy and reliability.

When a statistical model is applied to a dataset, it

attempts to explain the relationship between variables or

predict outcomes based on the data provided. The model

generates predictions, and these are compared to the actual

values observed in the dataset. The residuals are the

discrepancies between these two sets of numbers. In an ideal

scenario, a good model would have small residuals, indicating

that the predictions are close to the observed values.

In regression analysis, for instance, the residual sum of

squares (RSS) is a measure of the sum of the squares of the

基于句法的统计机器翻译综述

熊德意;刘群;林守勋

【摘 要】本文对基于句法的统计机器翻译进行了综述.按照模型所基于的语法不同,将基于句法的统计机器翻译分为两大类:基于形式化语法和基于语言学语法.对这两个不同类别,我们分别介绍它们代表性的工作,包括模型的构建、训练和解码器的设计等,并对比了各个模型的优点和缺点.最后我们对基于句法的统计机器翻译进行了总结,指出设计句法模型时要注意的问题,并对未来的发展趋势进行了预测.

【期刊名称】《中文信息学报》

【年(卷),期】2008(022)002

【总页数】12页(P28-39)

【关键词】人工智能;机器翻译;统计机器翻译;基于句法的统计机器翻译;树到串;树到树;依存语法

【作 者】熊德意;刘群;林守勋

【作者单位】中国科学院,计算技术研究所,北京,100080;中国科学院,研究生院,北京,100039;中国科学院,计算技术研究所,北京,100080;中国科学院,计算技术研究所,北京,100080

【正文语种】中 文

【中图分类】TP391

基于句法的统计机器翻译最早可以追溯到90年代初，当时同步树粘接语法（Synchronous Treeadjoining Grammar，简称STAG）［1］和反向转录语法（Inversion Transduction Grammar，简称ITG）［2］相继提出来并用到机器翻译上。从时间上说，与IBM提出基于单词的统计翻译模型的时间很接近，但是基于句法的统计机器翻译研究逐渐得到人们的关注和认可却是在2000年之后。即使是在这段时间，许多研究者对基于句法的统计机器翻译仍然持观望态度，主要是因为基于短语的统计机翻译仍然方兴未艾，最初的基于句法的统计机器翻译系统在性能上与基于短语的统计机器翻译系统相差甚远，再加上2003年由Och等人组织的约翰·霍普金斯大学（JHU）夏季研讨班“Syntax for Statistical Machine

machine dictionary 机器词典

machine language 机器语⾔

machine learning 机器学习

machine translation 机器翻译

machine-readable dictionary (MRD) 机读辞典

Macrolinguistics 宏观语⾔学

Markov chart 马可夫图

Mathematical Linguistics 数理语⾔学

maximum entropy 熵

M-D (modifier-head) construction 偏正结构

mean length of utterance (MLU) 语句平均长度

measure of information 讯习测度 [信息测度]

memory based 根据记忆的

mental lexicon ⼼理词汇库

mental model ⼼理模型

mental process ⼼理过程 [智⼒过程;智⼒处理]

metalanguage 超语⾔

metaphor 隐喻

metaphorical extension 隐喻扩展

metarule 律上律 [元规则]

metathesis 语⾳易位

Microlinguistics 微观语⾔学

middle structure 中间式结构

minimal pair 最⼩对

Minimalist Program 微⾔主义

MLU (mean length of utterance) 语句平均长度

modal 情态词

modal auxiliary 情态助动词

modal logic 情态逻辑

modifier 修饰语

Modular Logic Grammar 模组化逻辑语法

modular parsing system 模组化句法剖析系统

modularity 模组性(理论)

module 模组

monophthong 单元⾳monotonic 单调

monotonicity 单调性

Montague Grammar 蒙泰究语法 [蒙塔格语法]

研究NLP100篇必读的论⽂---已整理可直接下载

100篇必读的NLP论⽂

⾃⼰汇总的论⽂集，已更新链接：提取码：x7tnThis is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field shouldprobably 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 .本榜单由编制。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 Iposted years ago: .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?]( -are-the-most-important-research-paper -which-all-NLP-students-should- definitread)。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 and if anything is missing.请通过[pull requests]( papers/)和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。Machine LearningAvrim Blum and Tom Mitchell: Combining Labeled and Unlabeled Data with Co-Training, 1998.John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting andLabeling Sequence Data, ICML 2001.Charles Sutton, Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning.Kamal Nigam, et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999.Kevin Knight: Bayesian Inference with Tears, 2009.Marco Tulio Ribeiro et al.: "Why Should I Trust You?": Explaining the Predictions of Any Classifier, KDD 2016.Neural ModelsRichard Socher, et al.: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, NIPS 2011.Ronan Collobert et al.: Natural Language Processing (almost) from Scratch, J. of Machine Learning Research, 2011.Richard Socher, et al.: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, EMNLP 2013.Xiang Zhang, Junbo Zhao, and Yann LeCun: Character-level Convolutional Networks for Text Classification, NIPS 2015.Yoon Kim: Convolutional Neural Networks for Sentence Classification, 2014.Christopher Olah: Understanding LSTM Networks, 2015.Matthew E. Peters, et al.: Deep contextualized word representations, 2018.Jacob Devlin, et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.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Topic ModelsThomas Hofmann: Probabilistic Latent Semantic Indexing, SIGIR 1999.David Blei, Andrew Y. Ng, and Michael I. Jordan: Latent Dirichlet Allocation, J. Machine Learning Research, nguage ModelingJoshua Goodman: A bit of progress in language modeling, MSR Technical Report, 2001.Stanley F. Chen and Joshua Goodman: An Empirical Study of Smoothing Techniques for Language Modeling, ACL 2006.Yee Whye Teh: A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, COLING/ACL 2006.Yee Whye Teh: A Bayesian interpretation of Interpolated Kneser-Ney, 2006.Yoshua Bengio, et al.: A Neural Probabilistic Language Model, J. of Machine Learning Research, 2003.Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks, 2015.Yoon Kim, et al.: Character-Aware Neural Language Models, 2015.Segmentation, Tagging, ParsingDonald Hindle and Mats Rooth. Structural Ambiguity and Lexical Relations, Computational Linguistics, 1993.Adwait Ratnaparkhi: A Maximum Entropy Model for Part-Of-Speech Tagging, EMNLP 1996.Eugene Charniak: A Maximum-Entropy-Inspired Parser, NAACL 2000.Michael Collins: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms,EMNLP 2002.Dan Klein and Christopher Manning: Accurate Unlexicalized Parsing, ACL 2003.Dan Klein and Christopher Manning: Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency, ACL2004.Joakim Nivre and Mario Scholz: Deterministic Dependency Parsing of English Text, COLING 2004.Ryan McDonald et al.: Non-Projective Dependency Parsing using Spanning-Tree Algorithms, EMNLP 2005.Daniel Andor et al.: Globally Normalized Transition-Based Neural Networks, 2016.Oriol Vinyals, et al.: Grammar as a Foreign Language, 2015.Sequential Labeling & Information ExtractionMarti A. Hearst: Automatic Acquisition of Hyponyms from Large Text Corpora, COLING 1992.Collins and Singer: Unsupervised Models for Named Entity Classification, EMNLP 1999.Patrick Pantel and Dekang Lin, Discovering Word Senses from Text, SIGKDD, 2002.Mike Mintz et al.: Distant supervision for relation extraction without labeled data, ACL 2009.Zhiheng Huang et al.: Bidirectional LSTM-CRF Models for Sequence Tagging, 2015.