Atjaunināt sīkdatņu piekrišanu

Natural Language Processing for Online Applications: Text retrieval, extraction and categorization [Mīkstie vāki]

3.76/5 (23 ratings by Goodreads)
(Thomson Legal & Regulatory), (Thomson Legal & Regulatory)
  • Formāts: Paperback / softback, 226 pages, height x width: 240x160 mm, weight: 330 g
  • Sērija : Natural Language Processing 5 (1st)
  • Izdošanas datums: 20-Jun-2002
  • Izdevniecība: John Benjamins Publishing Co
  • ISBN-10: 1588112500
  • ISBN-13: 9781588112507
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 52,36 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Formāts: Paperback / softback, 226 pages, height x width: 240x160 mm, weight: 330 g
  • Sērija : Natural Language Processing 5 (1st)
  • Izdošanas datums: 20-Jun-2002
  • Izdevniecība: John Benjamins Publishing Co
  • ISBN-10: 1588112500
  • ISBN-13: 9781588112507
Citas grāmatas par šo tēmu:
This text covers the emerging technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical issues. It seeks to satisfy a need on the part of technology practitioners in the Internet space, faced with having to make difficult decisions as to what research has been done an what the best practices are. It is not intended as a vendor guide (such things are quickly out of date), or as a recipe for building applications (such recipes are very context-dependent). But it does identify the key technologies, the issues involved, and the strengths and weaknesses on evaluation in every chapter, both in terms of methodology (how to evaluate) and what controlled experimentation and industrial experience have to tell us.
Preface ix
Natural language processing
1(22)
What is NLP?
2(3)
NLP and linguistics
5(4)
Syntax and semantics
5(1)
Pragmatics and context
6(1)
Two views of NLP
7(1)
Tasks and supertasks
8(1)
Linguistic tools
9(8)
Sentence delimiters and tokenizers
9(2)
Stemmers and taggers
11(2)
Noun phrase and name recognizers
13(2)
Parsers and grammars
15(2)
Plan of the book
17(6)
Document retrieval
23(52)
Information retrieval
26(1)
Indexing technology
27(2)
Query processing
29(15)
Boolean search
29(3)
Ranked retrieval
32(4)
Probabilistic retrieval
36(6)
Language modeling
42(2)
Evaluating search engines
44(5)
Evaluation studies
44(1)
Evaluation metrics
45(2)
Relevance judgments
47(1)
Total system evaluation
48(1)
Attempts to enhance search performance
49(7)
Query expansion and thesauri
50(2)
Query expansion from relevance information*
52(4)
The future of Web searching
56(8)
Indexing the Web
57(2)
Searching the Web
59(3)
Ranking and reranking documents
62(1)
The state of online search
63(1)
Summary of information retrieval
64(11)
Information extraction
75(44)
The Message Understanding Conferences
76(2)
Regular expressions
78(3)
Finite automata in FASTUS
81(12)
Finite State Machines and regular languages
81(2)
Finite State Machines as parsers
83(10)
Pushdown automata and context-free grammars
93(13)
Analyzing case reports
93(2)
Context free grammars
95(2)
Parsing with a pushdown automaton
97(4)
Coping with incompleteness and ambiguity
101(5)
Limitations of current technology and future research
106(5)
Explicit versus implicit statements
107(2)
Machine learning for information extraction
109(1)
Statistical language models for information extraction
110(1)
Summary of information extraction
111(8)
Text categorization
119(54)
Overview of categorization tasks and methods
120(5)
Handcrafted rule based methods
125(2)
Inductive learning for text classification
127(21)
Naive Bayes classifiers
129(5)
Linear classifiers*
134(7)
Decision trees and decision lists
141(7)
Nearest Neighbor algorithms
148(2)
Combining classifiers
150(5)
Data fusion
150(1)
Boosting
151(2)
Using multiple classifiers
153(2)
Evaluation of text categorization systems
155(18)
Evaluation studies
155(2)
Evaluation metrics
157(5)
Relevance judgments
162(1)
System evaluation
163(10)
Towards text mining
173(46)
What is text mining?
174(4)
Reference and coreference
178(13)
Named entity recognition
180(6)
The coreference task
186(5)
Automatic summarization
191(17)
Summarization tasks
192(3)
Constructing summaries from document fragments
195(7)
Multi-document summarization (MDS)
202(6)
Testing of automatic summarization programs
208(3)
Evaluation problems in summarization research
208(1)
Building a corpus for training and testing
209(2)
Prospects for text mining and NLP
211(8)
Index 219