| 紀錄類型: |
書目-語言資料,印刷品
: 單行本
|
| 作者: |
MihalceaRada, 1974- |
| 合作者: |
RadevDragomir, 1968- |
| 出版地: |
Cambridge |
| 出版者: |
Cambridge University Press; |
| 出版年: |
2011 |
| 面頁冊數: |
viii, 192 p.ill. : 24 cm.; |
| 標題: |
Natural language processing (Computer science) - |
| 標題: |
Graphical user interfaces (Computer systems) - |
| 電子資源: |
http://catdir.loc.gov/catdir/enhancements/fy1101/2010044578-b.html |
| 電子資源: |
http://catdir.loc.gov/catdir/enhancements/fy1101/2010044578-d.html |
| 電子資源: |
http://catdir.loc.gov/catdir/enhancements/fy1101/2010044578-t.html |
| 摘要註: |
"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"--Provided by publisher |
| ISBN: |
978-0-521-89613-9bound |
| ISBN: |
0-521-89613-4bound |
| 內容註: |
Machine generated contents note: Part I. Introduction to Graph Theory: 1. Notations, properties, and representations; 2. Graph-based algorithms; Part II. Networks: 3. Random networks; 4. Language networks; Part III. Graph-Based Information Retrieval: 5. Link analysis for the world wide web; 6. Text clustering; Part IV. Graph-Based Natural Language Processing: 7. Semantics; 8. Syntax; 9. Applications |