The Optimal Reward Baseline for Policy-Gradient Reinforcement Learning

In this paper, we analyze some reinforcement learning methods, which are Value-based reinforcement learning ( VBRL), Policy-Gradient reinforcement learning and Actor-Critic reinforcement learning etc.

本文分析了几种强化学习方法，包括基于值函数（Value-Based）近似方法、策略梯度方法（Policy gradient）、以及Actor-Critic方法等。

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According to this framework, some current policy gradient algorithms are generalized. 2.

并且在上述理论框架的指导下，对现有的策略梯度算法进行了推广。

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Two fuzzy policy gradient reinforcement learning algorithms are proposed for Markov Decision Processes with discrete and continous actions, respectively.

本文分别针对具有离散行为空间和连续行为空间的马氏决策问题，提出了两种模糊策略梯度增强学习方法（Fuzzy Policy Gradient：FPG）。

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Currently, the main network security hot spot analysis system is based on natural language processing techniques of which the key analysis methods is retrieving key information from massive amounts of data based on theme-based model, LDA model and N-gram model.

当前主流的网络安全热点分析系统的研究和开发主要是基于自然语言处理技术，里面分析热点的关键方法是基于主题模型的LDA模型[4]和N-gram模型[7]从海量数据中提取重点信息。

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Now, chunk identification is widely used in many fields of natural language processing, especially in the example based machine translation ( EBMT), in which chunk identification is one of major techniques.

现在组块分析广泛用于自然语言处理的众多方面，尤其是在基于实例的机器翻译EBMT研究中，组块分析是重要技术之一。

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The operation document understanding is an important job in C3I System. Template-based method and natural-language-based method are most commonly used now, but these two methods are less flexible and cannot well express the semantic relationship in special fields.

作战文书的理解是C3I系统中的一项重要工作，目前普遍采用的是基于模板和基于自然语言处理两种方式，存在着适应性差、不能很好表示特定领域语义关系。

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Word Sense Disambiguation ( WSD) plays an important role in Natural Language Processing ( NLP). The study on WSD has great theoretical practical significance in Natural Language Understanding ( NLU) and now it has become a hotspot and nodus.

在自然语言处理（NLP）中，词义排歧（Word Sense Disambiguation）一直是研究的重点和难点，对其他的语言信息处理任务具有重要的理论和实践意义。

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Processing NIL text requires unconventional linguistic knowledge and techniques. Developed to handle formal language text, the existing natural language processing methods exhibit less effectiveness in dealing with NIL text.

NIL表达处理需要非常规知识和技术，而目前面向正规语言的自然语言处理技术在处理NIL文本时效果并不理想。

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According to the principle of use of Web information extraction can be divided into six kinds of way. For example, information extraction of based on wrapper, information extraction based on HTML structure, information extraction based on natural language processing, and so on.

根据采用的原理可以将Web信息抽取分为六种方式，例如基于包装器语言的信息抽取、基于HTML结构的信息抽取、基于自然语言处理方式的信息抽取等等。

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This article discusses the standardization stage of information processing& information content, which human beings have reached. Information content standardization depends on the integration and development of Knowledge Base construction and XML/ RDF for natural language processing.

本文论述了人类信息处理已经达到信息内容的标准化阶段，信息内容的标准化有赖于自然语言处理中的知识库建设和可扩展标记语言（XML）、资源描述框架（RDF）的融合与发展。

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The experiment makes clear that choosing domain concept as character item is better than others. Its F1 of macro-average is 79.35%, and its F1 of micro-average is 88.00%.

实验表明，在自然语言处理中，选择领域概念作为特征项，其宏平均下的F1值为79.35%，微平均下的F1值为88.00%。

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Two methods for collecting the structured data are discussed, which are natural language processing ( NLP) and structured data entry ( SDE).

阐述了两种结构化数据采集的方法：自然语言处理（NLP）和结构化数据输入（SDE）。

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These types of systems include advanced natural language processing systems that can discover new terms and relationships by analyzing content ( such as document text in ECM systems).

这种系统包括高级的自然语言处理系统，它能够通过分析内容（比如ECM系统中的文档文本）发现新的词汇和关系。

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Lots of advanced AI techniques such as machine learning, knowledge representation, pattern identification, genetic algorithms and distributed intelligent systems have been applied to the research ofDSS.

许多先进的人工智能技术如机器学习、知识表示、自然语言处理、模式识别、遗传算法及分布式智能系统都被融入DSS的研究中。

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Co-Training, as an alternative to EM algorithm, is a well-known form of bootstrapping which is a topic of interest in NLP.

作为EM算法的替代，Co-Training是众所周知的自举算法，近来已经成为自然语言处理领域的兴趣焦点。

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Intelligent question answering system based on XML reasoned using repository, it is a combinative technique of AI, information searches and disposal of nature language and so on, it has many advantages such as intelligence, opening and etc.

基于XML的智能答疑系统利用知识库进行推理，是一个智能适应性的知识库系统，它是人工智能、信息检索和自然语言处理等技术的结合，具有智能性、开放性等优点。

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Search engine become the most commonly used information retrieval tool, it involves a database, information retrieval, artificial only, data mining, Java, natural language processing and other fields of theory and technology.

搜索引擎成为人们最常用的信息检索工具，它涉及数据库、信息检索、人工只能、数据挖掘、Java、自然语言处理等多个领域的理论和技术。

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In the field of natural language processing, entity recognition is the key technique in many Chinese information Processing applications such as information extraction, syntactic analysis, machine translation, metadata annotate for Semantic Web and so on.

在自然语言处理领域，实体识别是信息提取、句法分析、机器翻译、面向SEMANTIC WEB的元数据标注等应用领域重要的基础性工具。

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SGML and its application in natural language processing

标准通用置标语言SGML及其在自然语言处理中的应用

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ICM RME receives this message and processes it in two main phases: a multilingual Natural Language Processing ( NLP) phase, and a language-independent statistic Concept Modeling phase.

ICM RME接收这个消息并分两个主要阶段处理它：多语种的自然语言处理（Natural Language Processing，NLP）阶段，以及独立于语言的统计性的概念建模（Concept Modeling）阶段。

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