natural language processing的例句大全

Ing is a vital and practical information processing task in natural language processing, and becomes an important problem in domains such as Internet information retrieval.

自动文摘研究作为自然语言处理研究的一个重要且实用的分支，目前逐渐成为Internet信息检索等应用领域的重要研究课题之一。

Document clustering is an important research topic of natural language processing and is widely applicable in areas such as information retrieval, web mining and digital libraries.

文本聚类是自然语言处理研究中一项重要研究课题，文本聚类技术广泛地应用于信息检索、Web挖掘和数字图书馆等领域。

As a core issue of natural language processing ( NLP), high-performance syntactic analysis played an important role in NLP applications.

作为自然语言处理（Natural Language Processing，NLP）领域的一个核心问题，高性能句法分析在NLP应用领域中发挥了重要的作用。

It is important to draw a distinction between natural language processing ( NLP) and specific n-gram grammars.

一定要对自然语言处理（NLP）和特定的n元语法加以区别。

With the rapid growth of Internet and increase of electronic text, automatic text classification ( ATC) has been the hottest research issues in information retrieval and natural language processing.

随着Internet的迅猛发展和电子文档信息的不断丰富，文档自动分类日益成为信息检索和自然语言处理领域的研究热点。

CIP aims to be a natural language processing suit of Chinese.

CIP目标是成为中文的自然语言处理套件。

Web text mining deals with structure and unstructured data of the web to discover knowledge by use of data mining, machine learning, natural language processing, information retrieval, knowledge management.

Web文本挖掘采用数据挖掘、机器学习、自然语言处理、信息检索和知识管理等领域的技术来处理和分析非结构或半结构化的文本，从中提取有价值的知识。

Open domain question answering ( QA) represents a challenge of natural language processing, aiming at returning exact answers in response to natural language questions.

开放领域的问题回答（Question Answering）是自然语言处理领域中具有挑战性的研究方向。

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。

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

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。

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。

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。

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）。

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。

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。

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。

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。

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。

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。