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E-grāmata: Advanced Applications of Natural Language Processing for Performing Information Extraction

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  • Formāts: PDF+DRM
  • Sērija : SpringerBriefs in Speech Technology
  • Izdošanas datums: 06-May-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319155630
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Advanced Applications of Natural Language Processing for Performing Information ExtractionPalielināt
  • Formāts: PDF+DRM
  • Sērija : SpringerBriefs in Speech Technology
  • Izdošanas datums: 06-May-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319155630
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This book explains how can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web. Readers are introduced to the problem of IE and its current challenges and limitations, supported with examples. The book discusses the need to fill the gap between documents, data, and people, and provides a broad overview of the technology supporting IE. The authors present a generic architecture for developing systems that are able to learn how to extract relevant information from natural language documents, and illustrate how to implement working systems using state-of-the-art and freely available software tools. The book also discusses concrete applications illustrating IE uses.

 

·         Provides an overview of state-of-the-art technology in information extraction (IE), discussing achievements and limitations for the software developer and providing references for specialized literature in the area

·         Presents a comprehensive list of freely available, high quality software for several subtasks of IE and for several natural languages

·         Describes a generic architecture that can learn how to extract information for a given application domain
1 Introduction
1(12)
1.1 Document Society
1(1)
1.2 Problems
2(1)
1.3 Semantics and Knowledge Representation
3(1)
1.4 Natural Language Processing
4(1)
1.5 Information Extraction
5(3)
1.5.1 Main Challenges in Information Extraction
5(1)
1.5.2 Approaches to Information Extraction
6(1)
1.5.3 Performance Measures
7(1)
1.5.4 General Architecture for Information Extraction
8(1)
1.6 Book Structure
8(5)
References
10(3)
2 Data Gathering, Preparation and Enrichment
13(14)
2.1 Process Overview
13(2)
2.2 Tokenization and Sentence Boundary Detection
15(2)
2.2.1 Tools
15(1)
2.2.2 Representative Tools: Punkt and iSentenizer
16(1)
2.3 Morphological Analysis and Part-of-Speech Tagging
17(3)
2.3.1 Tools
18(1)
2.3.2 Representative Tools: Stanford POS Tagger, SVMTool, and TreeTagger
19(1)
2.4 Syntactic Parsing
20(3)
2.4.1 Representative Tools: Epic, StanfordParser, MaltParser, TurboParser
21(2)
2.5 Representative Software Suites
23(4)
2.5.1 Stanford NLP
23(1)
2.5.2 Natural Language Toolkit (NLTK)
24(1)
2.5.3 GATE
24(1)
References
24(3)
3 Identifying Things, Relations, and Semantizing Data
27(10)
3.1 Identifying the Who, the Where, and the When
27(3)
3.2 Relating Who, What, When, and Where
30(2)
3.3 Getting Everything Together
32(5)
3.3.1 Ontology
32(1)
3.3.2 Ontology-Based Information Extraction (OBIE)
33(1)
References
34(3)
4 Extracting Relevant Information Using a Given Semantic
37(14)
4.1 Introduction
37(1)
4.2 Defining How and What Information Will Be Extracted
38(1)
4.3 Architecture
39(1)
4.4 Implementation of a Prototype Using State-of-the-Art Tools
40(11)
4.4.1 Natural Language Processing
41(3)
4.4.2 Domain Representation
44(1)
4.4.3 Semantic Extraction and Integration
45(4)
References
49(2)
5 Application Examples
51(20)
5.1 A Tutorial Example
51(7)
5.1.1 Selecting and Obtaining Software Tools
53(1)
5.1.2 Tools Setup
53(1)
5.1.3 Processing the Target Document
54(4)
5.1.4 Using for Other Languages and for Syntactic Parsing
58(1)
5.2 Application Example 2: IE Applied to Electronic Government
58(13)
5.2.1 Goals
58(1)
5.2.2 Documents
59(1)
5.2.3 Obtaining the Documents
59(2)
5.2.4 Application Setup
61(4)
5.2.5 Making Available Extracted Information Using a Map
65(2)
5.2.6 Conducting Semantic Information Queries
67(1)
References
68(3)
6 Conclusion
71(2)
Index 73
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