最大熵模型（Maximum Entropy Model）是一种机器学习方法，在自然语言处理的许多领域（如词性标注、中文分词、句子边界识别、浅层句法分析及文本分类等）都有比较好的应用效果。张乐博士的最大熵模型工具包manual里有“Further Reading”，写得不错，就放到这里作为最大熵模型文献阅读指南了。
与《统计机器翻译文献阅读指南》不同，由于自己也正在努力学习Maximum Entropy Model中，没啥发言权，就不多说废话了。这些文献在Google上很容易找到，不过多数都比较长（30多页），甚至有两篇是博士论文，有100多页，希望初学读者不要被吓住了，毕竟经典的东西是值得反复推敲的！
Maximum Entropy Model Tutorial Reading
This section lists some recommended papers for your further reference.
1. Maximum Entropy Approach to Natural Language Processing [Berger et al., 1996]
（必读）A must read paper on applying maxent technique to Natural Language Processing. This paper describes maxent in detail and presents an Increment Feature Selection algorithm for increasingly construct a maxent model as well as several example in statistical Machine Translation.
2.Inducing Features of Random Fields [Della Pietra et al., 1997]
（必读）Another must read paper on maxent. It deals with a more general frame work: Random Fields and proposes an Improved Iterative Scaling algorithm for estimating parameters of Random Fields. This paper gives theoretical background to Random Fields (and hence Maxent model). A greedy Field Induction method is presented to automatically construct a detail random elds from a set of atomic features. An word morphology application for English is developed.
3.Adaptive Statistical Language Modeling: A Maximum Entropy Approach [Rosenfeld, 1996]
This paper applied ME technique to statistical language modeling task. More specically, it built a conditional Maximum Entropy model that incorporated traditional N-gram, distant N-gram and trigger pair features. Significantly perplexity reduction over baseline trigram model was reported. Later, Rosenfeld and his group proposed a Whole Sentence Exponential Model that overcome the computation bottleneck of conditional ME model.
4.Maximum Entropy Models For Natural Language Ambiguity Resolution [Ratnaparkhi, 1998]
This dissertation discussed the application of maxent model to various Natural Language Disambiguity tasks in detail. Several problems were attacked within the ME framework: sentence boundary detection, part-of-speech tagging, shallow parsing and text categorization. Comparison with other machine learning technique (Naive Bayes, Transform Based Learning, Decision Tree etc.) are given.
5.The Improved Iterative Scaling Algorithm: A Gentle Introduction [Berger, 1997]
This paper describes IIS algorithm in detail. The description is easier to understand than [Della Pietra et al., 1997], which involves more mathematical notations.
6.Stochastic Attribute-Value Grammars (Abney, 1997)
Abney applied Improved Iterative Scaling algorithm to parameters estimation of Attribute-Value grammars, which can not be corrected calculated by ERF method (though it works on PCFG). Random Fields is the model of choice here with a general Metropolis-Hasting Sampling on calculating feature expectation under newly constructed model.
7.A comparison of algorithms for maximum entropy parameter estimation [Malouf, 2003]
Four iterative parameter estimation algorithms were compared on several NLP tasks. L-BFGS was observed to be the most effective parameter estimation method for Maximum Entropy model, much better than IIS and GIS. [Wallach, 2002] reported similar results on parameter estimation of Conditional Random Fields.
1.MaxEnt and Exponential Models
2.A maxent reading list