Graph-based Natural Language Processing and Information Retrieval 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Rada F. Mihalcea 作者

Cambridge University Press

译者

2011-4-11 出版日期

208 页数

USD 65.00 价格

Hardcover

丛书系列

9780521896139 图书编码

Graph-based Natural Language Processing and Information Retrieval 在线电子书图书标签: 计算语言学 NLP 机器学习 graph-based 数学复杂网络统计学习 IR

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Graph-based Natural Language Processing and Information Retrieval 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Graph-based Natural Language Processing and Information Retrieval 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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评分☆☆☆☆☆

2011年，还比较新，小书一本，查询用不错

评分☆☆☆☆☆

降一星，有点唬人的书。比较表面，不过还是很前沿滴。overview吧~

评分☆☆☆☆☆

降一星，有点唬人的书。比较表面，不过还是很前沿滴。overview吧~

评分☆☆☆☆☆

降一星，有点唬人的书。比较表面，不过还是很前沿滴。overview吧~

评分☆☆☆☆☆

2011年，还比较新，小书一本，查询用不错

Graph-based Natural Language Processing and Information Retrieval 在线电子书著者简介

Graph-based Natural Language Processing and Information Retrieval 在线电子书图书目录

Graph-based Natural Language Processing and Information Retrieval 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Graph-based Natural Language Processing and Information Retrieval 在线电子书图书描述

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

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