Atjaunināt sīkdatņu piekrišanu

Natural Language Processing for Online Applications: Text retrieval, extraction and categorization. Second revised edition 2nd Revised edition [Hardback]

3.81/5 (16 ratings by Goodreads)
(Thomson Corporation), (Thomson Corporation)
  • Formāts: Hardback, 231 pages, height x width: 245x164 mm, weight: 590 g
  • Sērija : Natural Language Processing 5
  • Izdošanas datums: 05-Jun-2007
  • Izdevniecība: John Benjamins Publishing Co
  • ISBN-10: 902724992X
  • ISBN-13: 9789027249920
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 124,84 €*
  • * 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
Natural Language Processing for Online Applications: Text retrieval, extraction and categorization. Second revised edition 2nd Revised editionPalielināt
  • Formāts: Hardback, 231 pages, height x width: 245x164 mm, weight: 590 g
  • Sērija : Natural Language Processing 5
  • Izdošanas datums: 05-Jun-2007
  • Izdevniecība: John Benjamins Publishing Co
  • ISBN-10: 902724992X
  • ISBN-13: 9789027249920
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789027249920.html
This text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation.
Preface to the 2nd edition IX
CHAPTER 1 Natural language processing 1
1.1 What is NLP?
2
1.2 NLP and linguistics
5
1.2.1 Syntax and semantics
5
1.2.2 Pragmatics and context
6
1.2.3 Two views of NLP
7
1.2.4 Tasks and supertasks
8
1.3 Linguistic tools
11
1.3.1 Sentence delimiters and tokenizers
11
1.3.2 Stemmers and taggers
13
1.3.3 Noun phrase and name recognizers
16
1.3.4 Parsers and grammars
17
1.4 Plan of the book
20
CHAPTER 2 Document retrieval 23
2.1 Information retrieval
24
2.2 Indexing technology
25
2.3 Query processing
27
2.3.1 Boolean search
27
2.3.2 Ranked retrieval
30
2.3.3 Probabilistic retrieval
33
2.3.4 Language modeling*
40
2.4 Evaluating search engines
45
2.4.1 Evaluation studies
45
2.4.2 Evaluation metrics
46
2.4.3 Relevance judgments
48
2.4.4 Total system evaluation
51
2.5 Attempts to enhance search performance
52
2.5.1 Query expansion and thesauri
52
2.5.2 Query expansion from relevance information*
55
2.6 The future of Web searching
59
2.6.1 Leveraging link structure 6o
2.6.2 Ranking and reranking documents
63
2.6.3 The future of online search
64
CHAPTER 3 Information extraction 69
3.1 The message understanding conferences
70
3.2 Regular expressions
73
3.2.1 Regexs defined
73
3.2.2 Regexs in use
74
3.3 Finite automata in FASTUS
75
3.3.1 Finite state machines and regular languages
76
3.3.2 Finite state machines as parsers
78
3.3.3 The GATE toolkit
88
3.4 Context-free grammars
92
3.4.1 Analyzing case reports
92
3.4.2 Pushdown automata and context free grammars
94
3.4.3 Parsing with a dynamic programming algorithm
97
3.4.4 Coping with incompleteness and ambiguity
102
3.4.5 Template filling and conflict detection
103
3.5 Limitations of current technology and future research
104
3.5.1 Explicit versus implicit statements
106
3.5.2 Machine learning for information extraction
107
3.5.3 Statistical language models for information extraction
108
3.6 Summary of information extraction
110
CHAPTER 4 Text categorization 113
4.1 Overview of categorization tasks
115
4.2 Handcrafted rule based methods
120
4.3 Inductive learning for text classification
122
4.3.1 Naive Bayes classifiers
123
4.3.2 Linear classifiers*
129
4.3.3 Decision trees and decision lists
137
4.4 Nearest neighbor algorithms
144
4.5 Combining classifiers
147
4.6 Evaluation of text categorization systems
154
4.6.1 Evaluation studies
154
4.6.2 Evaluation metrics
156
4.6.3 Evaluating effectiveness
161
CHAPTER 5 Text mining 163
5.1 What is text mining?
164
5.2 Resolving reference and coreference
168
5.2.1 Named entity recognition
170
5.2.2 The coreference task
178
5.3 Automatic summarization
183
5.3.1 Summarization tasks
184
5.3.2 Constructing summaries from document fragments
188
5.3.3 Multi-document summarization (MDS)
196
5.3.4 Topic detection and tracking
199
5.3.5 Multimedia and multilingual summarization
204
5.4 Testing of automatic summarization programs
204
5.4.1 Evaluation issues in summarization research
205
5.4.2 Building a corpus for training and testing
207
5.4.3 Summarization meets question answering at DUC
208
5.5 Prospects for text mining and NLP
210
References 215
Index 227