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E-grāmata: Natural Language Processing: Semantic Aspects [Taylor & Francis e-book]

(University of Westminster, London, UK), , (Babes-Bolyai University, Cluj-Napoca, Romania)
  • Formāts: 346 pages, 13 Illustrations, black and white
  • Izdošanas datums: 14-Nov-2013
  • Izdevniecība: CRC Press Inc
  • ISBN-13: 9780429072536
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Natural Language Processing: Semantic AspectsPalielināt
  • Formāts: 346 pages, 13 Illustrations, black and white
  • Izdošanas datums: 14-Nov-2013
  • Izdevniecība: CRC Press Inc
  • ISBN-13: 9780429072536
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9780429072536
"This book introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language,the and the nature of language. The book also emphasizes the portions of mathematics needed to understand the discussed algorithms. "--

Emphasizing the semantic dimension, Kapentanios, Tatar, and Sacarea share insights they have acquired doing research at the crossroads of natural language processing, information retrieval and search, text mining, knowledge representation, formal concept analysis, and further mathematical areas. Among their topics are algebraic structures, measuring word meaning similarity, multi-lingual querying and parametric theory, text summarization, and named entity recognition. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

This book introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, and the nature of language. The book also emphasizes the portions of mathematics needed to understand the discussed algorithms.

Preface v
Part I Introduction
1 The Nature of Language
3(16)
1.1 Syntax versus Semantics
3(5)
1.2 Meaning and Context
8(5)
1.3 The Symbol Grounding Problem
13(6)
Part II Mathematics
2 Relations
19(24)
2.1 Operations with Relations
22(3)
2.2 Homogenous Relations
25(7)
2.3 Order Relations
32(2)
2.4 Lattices. Complete Lattices
34(2)
2.5 Graphical Representation of Ordered Sets
36(2)
2.6 Closure Systems. Galois Connections
38(5)
3 Algebraic Structures
43(25)
3.1 Functions
43(3)
3.2 Binary Operations
46(1)
3.3 Associative Operations. Semigroups
47(2)
3.4 Neutral Elements. Monoids
49(1)
3.5 Morphisms
50(4)
3.6 Invertible Elements. Groups
54(4)
3.7 Subgroups
58(3)
3.8 Group Morphisms
61(3)
3.9 Congruence Relations
64(1)
3.10 Rings and Fields
65(3)
4 Linear Algebra
68(26)
4.1 Vectors
68(2)
4.2 The space Rn
70(2)
4.3 Vector Spaces Over Arbitrary Fields
72(2)
4.4 Linear and Affine Subspaces
74(5)
4.5 Linearly Independent Vectors. Generator Systems. Basis
79(15)
4.5.1 Every vector space has a basis
83(7)
4.5.2 Algorithm for computing the basis of a generated sub-space
90(4)
5 Conceptual Knowledge Processing and Formal Concept Analysis
94(27)
5.1 Introduction
94(2)
5.2 Context and Concept
96(10)
5.3 Many-valued Contexts
106(1)
5.4 Finding all Concepts
107(14)
Part III Knowledge Representation for NLP
6 Measuring Word Meaning Similarity
121(8)
6.1 Introduction
121(1)
6.2 Baseline Methods and Algorithms
122(6)
6.2.1 Intertwining space models and metrics
122(4)
6.2.2 Measuring similarity
126(2)
6.3 Summary and Main Conclusions
128(1)
7 Semantics and Query Languages
129(33)
7.1 Introduction
129(2)
7.2 Baseline Methods and Algorithms
131(29)
7.2.1 The methodology
131(2)
7.2.2 The theory on semantics
133(4)
7.2.3 Automata theory and (query) languages
137(8)
7.2.4 Exemplary algorithms and data structures
145(15)
7.3 Summary and Major Conclusions
160(2)
8 Multi-Lingual Querying and Parametric Theory
162(21)
8.1 Introduction
162(2)
8.2 Baseline Methods and Algorithms
164(15)
8.2.1 Background theory
164(4)
8.2.2 An example
168(2)
8.2.3 An indicative approach
170(4)
8.2.4 An indicative system architecture and implementation
174(5)
8.3 Summary and Major Conclusions
179(4)
Part IV Knowledge Extraction and Engineering for NLP
9 Word Sense Disambiguation
183(30)
9.1 Introduction
183(3)
9.1.1 Meaning and context
184(2)
9.2 Methods and Algorithms: Vectorial Methods in WSD
186(6)
9.2.1 Associating vectors to the contexts
186(2)
9.2.2 Measures of similarity
188(1)
9.2.3 Supervised learning of WSD by vectorial methods
189(1)
9.2.4 Unsupervised approach. Clustering contexts by vectorial method
190(2)
9.3 Methods and Algorithms: Non-vectorial Methods in WSD
192(1)
9.3.1 Naive Bayes classifier approach to WSD
192(1)
9.4 Methods and Algorithms: Bootstrapping Approach of WSD
193(3)
9.5 Methods and Algorithms: Dictionary-based Disambiguation
196(11)
9.5.1 Lesk's algorithms
196(1)
9.5.2 Yarowsky's bootstrapping algorithm
197(1)
9.5.3 WordNet-based methods
198(9)
9.6 Evaluation of WSD Task
207(3)
9.6.1 The benefits of WSD
209(1)
9.7 Conclusions and Recent Research
210(3)
10 Text Entailment
213(18)
10.1 Introduction
213(1)
10.2 Methods and Algorithms: A Survey of RTE-1 and RTE-2
214(9)
10.2.1 Logical aspect of TE
216(2)
10.2.2 Logical approaches in RTE-1 and RTE-2
218(1)
10.2.3 The directional character of the entailment relation and some directional methods in RTE-1 and RTE-2
218(2)
10.2.4 Text entailment recognition by similarities between words and texts
220(3)
10.2.5 A few words about RTE-3 and the last RTE challenges
223(1)
10.3 Proposal for Direct Comparison Criterion
223(6)
10.3.1 Lexical refutation
224(3)
10.3.2 Directional similarity of texts and the comparison criterion
227(1)
10.3.3 Two more examples of the comparison criterion
228(1)
10.4 Conclusions and Recent Research
229(2)
11 Text Segmentation
231(31)
11.1 Introduction
231(2)
11.1.1 Topic segmentation
232(1)
11.2 Methods and Algorithms
233(23)
11.2.1 Discourse structure and hierarchical segmentation
233(3)
11.2.2 Linear segmentation
236(8)
11.2.3 Linear segmentation by Lexical Chains
244(4)
11.2.4 Linear segmentation by FCA
248(8)
11.3 Evaluation
256(4)
11.4 Conclusions and Recent Research
260(2)
12 Text Summarization
262(35)
12.1 Introduction
262(5)
12.2 Methods and Algorithms
267(20)
12.2.1 Summarization starting from linear segmentation
267(4)
12.2.2 Summarization by Lexical Chains (LCs)
271(3)
12.2.3 Methods based on discourse structure
274(1)
12.2.4 Summarization by FCA
275(5)
12.2.5 Summarization by sentence clustering
280(3)
12.2.6 Other approaches
283(4)
12.3 Multi-document Summarization
287(4)
12.4 Evaluation
291(4)
12.4.1 Conferences and Corpora
294(1)
12.5 Conclusions and Recent Research
295(2)
13 Named Entity Recognition
297(14)
13.1 Introduction
297(1)
13.2 Baseline Methods and Algorithms
298(11)
13.2.1 Hand-crafted rules based techniques
298(5)
13.2.2 Machine learning techniques
303(6)
13.3 Summary and Main Conclusions
309(2)
Bibliography 311(20)
Index 331
Epaminondas Kapetanios, Doina Tatar, Christian Sacarea
Permanent link: https://www.kriso.lv/db/9780429072536_pe.html
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