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This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks.

The first half of this book describes the basic NLP processing tools: tokenization, part-of-speech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. The second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. These chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. The book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future.

Acknowledgments xiii
1 Introduction
1(8)
1.1 Information Extraction
2(2)
1.2 Ambiguity
4(1)
1.3 Performance
5(2)
1.4 Structure of the Book
7(2)
2 Linguistic Processing
9(16)
2.1 Introduction
9(1)
2.2 Approaches to Linguistic Processing
9(1)
2.3 NLP Pipelines
10(2)
2.4 Tokenization
12(2)
2.5 Sentence Splitting
14(1)
2.6 POS Tagging
15(1)
2.7 Morphological Analysis and Stemming
16(3)
2.7.1 Stemming
17(2)
2.8 Syntactic Parsing
19(2)
2.9 Chunking
21(2)
2.10 Summary
23(2)
3 Named Entity Recognition and Classification
25(12)
3.1 Introduction
25(1)
3.2 Types of Named Entities
26(1)
3.3 Named Entity Evaluations and Corpora
27(1)
3.4 Challenges in NERC
27(2)
3.5 Related Tasks
29(1)
3.6 Approaches to NERC
30(3)
3.6.1 Rule-based Approaches to NERC
30(1)
3.6.2 Supervised Learning Methods for NERC
31(2)
3.7 Tools for NERC
33(1)
3.8 NERC on Social Media
34(1)
3.9 Performance
34(1)
3.10 Summary
35(2)
4 Relation Extraction
37(16)
4.1 Introduction
37(1)
4.2 Relation Extraction Pipeline
37(2)
4.3 Relationship between Relation Extraction and other IE Tasks
39(1)
4.4 The Role of Knowledge Bases in Relation Extraction
40(1)
4.5 Relation Schemas
41(1)
4.6 Relation Extraction Methods
42(2)
4.6.1 Bootstrapping Approaches
42(2)
4.7 Rule-based Approaches
44(1)
4.8 Supervised Approaches
45(1)
4.9 Unsupervised Approaches
46(1)
4.10 Distant Supervision Approaches
47(2)
4.10.1 Universal Schemas
48(1)
4.10.2 Hybrid Approaches
49(1)
4.11 Performance
49(1)
4.12 Summary
50(3)
5 Entity Linking
53(8)
5.1 Named Entity Linking and Semantic Linking
54(1)
5.2 NEL Datasets
54(1)
5.3 LOD-based Approaches
55(3)
5.3.1 DBpedia Spotlight
55(1)
5.3.2 YODIE: A LOD-based Entity Disambiguation Framework
56(1)
5.3.3 Other Key LOD-based Approaches
57(1)
5.4 Commercial Entity Linking Services
58(1)
5.5 NEL for Social Media Content
59(1)
5.6 Discussion
60(1)
6 Automated Ontology Development
61(12)
6.1 Introduction
61(1)
6.2 Basic Principles
61(2)
6.3 Term Extraction
63(3)
6.3.1 Approaches Using Distributional Knowledge
64(1)
6.3.2 Approaches Using Contextual Knowledge
65(1)
6.4 Relation Extraction
66(3)
6.4.1 Clustering Methods
66(1)
6.4.2 Semantic Relations
66(2)
6.4.3 Lexico-syntactic Patterns
68(1)
6.4.4 Statistical Techniques
68(1)
6.5 Enriching Ontologies
69(1)
6.6 Ontology Development Tools
70(1)
6.6.1 Text2Onto
70(1)
6.6.2 SPRAT
70(1)
6.6.3 FRED
70(1)
6.6.4 Semi-automatic Ontology Creation
71(1)
6.7 Summary
71(2)
7 Sentiment Analysis
73(14)
7.1 Introduction
73(2)
7.2 Issues in Opinion Mining
75(1)
7.3 Opinion-Mining Subtasks
76(3)
7.3.1 Polarity Recognition
76(1)
7.3.2 Opinion Target Detection
76(1)
7.3.3 Opinion Holder Detection
77(1)
7.3.4 Sentiment Aggregation
77(1)
7.3.5 Further Linguistic Subcomponents
78(1)
7.4 Emotion Detection
79(2)
7.5 Methods for Opinion Mining
81(2)
7.6 Opinion Mining and Ontologies
83(2)
7.7 Opinion-Mining Tools
85(1)
7.8 Summary
86(1)
8 NLP for Social Media
87(20)
8.1 Social Media Streams: Characteristics, Challenges, and Opportunities
88(2)
8.2 Ontologies for Representing Social Media Semantics
90(2)
8.3 Semantic Annotation of Social Media
92(15)
8.3.1 Keyphrase Extraction
92(1)
8.3.2 Ontology-based Entity Recognition in Social Media
93(6)
8.3.3 Event Detection
99(1)
8.3.4 Sentiment Detection and Opinion Mining
100(1)
8.3.5 Cross-media Linking
101(1)
8.3.6 Rumor Analysis
102(1)
8.3.7 Discussion
103(4)
9 Applications
107(28)
9.1 Semantic Search
107(15)
9.1.1 What is Semantic Search?
108(1)
9.1.2 Why Semantic Full-text Search?
109(1)
9.1.3 Semantic Search Queries
110(1)
9.1.4 Relevance Scoring and Retrieval
111(1)
9.1.5 Semantic Search Full-text Platforms
111(3)
9.1.6 Ontology-based Faceted Search
114(2)
9.1.7 Form-based Semantic Search Interfaces
116(2)
9.1.8 Semantic Search over Social Media Streams
118(4)
9.2 Semantic-Based User Modeling
122(3)
9.2.1 Constructing Social Semantic User Models from Semantic Annotations
122(3)
9.2.2 Discussion
125(1)
9.3 Filtering and Recommendations for Social Media Streams
125(1)
9.4 Browsing and Visualization of Social Media Streams
126(6)
9.5 Discussion and Future Work
132(3)
10 Conclusions
135(6)
10.1 Summary
135(1)
10.2 Future Directions
135(6)
10.2.1 Cross-media Aggregation and Multilinguality
136(1)
10.2.2 Integration and Background Knowledge
137(1)
10.2.3 Scalability and Robustness
137(1)
10.2.4 Evaluation, Shared Datasets, and Crowdsourcing
138(3)
Bibliography 141(38)
Authors' Biographies 179