
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title: Text Algorithms
- Author(s) Maxime Crochemore and Wojciech Rytter
- Publisher: Oxford University Press, USA (October 20, 1994)
- Hardcover: 432 pages
- eBook: PDF, ePub, Kindle, etc.
- Language: English
- ISBN-10: 0195086090
- ISBN-13: 978-0195086096
- Share This:
![]() |
This much-needed book on the design of algorithms and data structures for text processing emphasizes both theoretical foundations and practical applications.
Throughout, the book emphasizes the efficiency of algorithms, holding that the essence of their usefulness depends on it. This is especially important since the algorithms described here will find application in "Big Science" areas like molecular sequence analysis where the explosive growth of data has caused problems for the current generation of software.
Finally, with its development of theoretical background, the book can be considered as a mathematical foundation for the analysis and production of text processing algorithms.
About the Authors- N/A
- Algorithms and Data Structures
- Information Retrieval (IR) and Search Engines Design/Implementation
- Data Analysis and Data Mining, Big Data

-
Theory and Applications for Advanced Text Mining (S. Sakurai)
This book introduces advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. Text mining techniques have been studied aggressively in order to extract the knowledge from the data.
-
Clinical Text Mining: Secondary Use of Electronic Patient Records
This book describes the results of Natural Language Processing (NLP) and machine learning methods applied to clinical text from electronic patient records, provides a comprehensive overview of technical issues arising in clinical text mining.
-
Text Mining with R: A Tidy Approach (Julia Silge, et al)
You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.
-
Data-Intensive Text Processing with MapReduce (Jimmy Lin)
This free book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
-
Text Processing in Python (David Mertz)
This book is an example-driven, hands-on tutorial that carefully teaches programmers how to accomplish numerous text processing tasks using the Python language. It provides efficient and effective solutions to specific text processing problems.
:
|
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |