Natural Language Processing for the Semantic Web. Diana Maynard

       6.1 Introduction

       6.2 Basic Principles

       6.3 Term Extraction

       6.3.1 Approaches Using Distributional Knowledge

       6.3.2 Approaches Using Contextual Knowledge

       6.4 Relation Extraction

       6.4.1 Clustering Methods

       6.4.2 Semantic Relations

       6.4.3 Lexico-syntactic Patterns

       6.4.4 Statistical Techniques

       6.5 Enriching Ontologies

       6.6 Ontology Development Tools

       6.6.1 Text2Onto

       6.6.2 SPRAT

       6.6.3 FRED

       6.6.4 Semi-automatic Ontology Creation

       6.7 Summary

       7 Sentiment Analysis

       7.1 Introduction

       7.2 Issues in Opinion Mining

       7.3 Opinion-Mining Subtasks

       7.3.1 Polarity Recognition

       7.3.2 Opinion Target Detection

       7.3.3 Opinion Holder Detection

       7.3.4 Sentiment Aggregation

       7.3.5 Further Linguistic Subcomponents

       7.4 Emotion Detection

       7.5 Methods for Opinion Mining

       7.6 Opinion Mining and Ontologies

       7.7 Opinion-Mining Tools

       7.8 Summary

       8 NLP for Social Media

       8.1 Social Media Streams: Characteristics, Challenges, and Opportunities

       8.2 Ontologies for Representing Social Media Semantics

       8.3 Semantic Annotation of Social Media

       8.3.1 Keyphrase Extraction

       8.3.2 Ontology-based Entity Recognition in Social Media

       8.3.3 Event Detection

       8.3.4 Sentiment Detection and Opinion Mining

       8.3.5 Cross-media Linking

       8.3.6 Rumor Analysis

       8.3.7 Discussion

       9 Applications

       9.1 Semantic Search

       9.1.1 What is Semantic Search?

       9.1.2 Why Semantic Full-text Search?

       9.1.3 Semantic Search Queries

       9.1.4 Relevance Scoring and Retrieval

       9.1.5 Semantic Search Full-text Platforms

       9.1.6 Ontology-based Faceted Search

       9.1.7 Form-based Semantic Search Interfaces

       9.1.8 Semantic Search over Social Media Streams

       9.2 Semantic-Based User Modeling

       9.2.1 Constructing Social Semantic User Models from Semantic Annotations

       9.2.2 Discussion

       9.3 Filtering and Recommendations for Social Media Streams

       9.4 Browsing and Visualization of Social Media Streams

       9.5 Discussion and Future Work

       10 Conclusions

       10.1 Summary

       10.2 Future Directions

       10.2.1 Cross-media Aggregation and Multilinguality

       10.2.2 Integration and Background Knowledge

       10.2.3 Scalability and Robustness

       10.2.4 Evaluation, Shared Datasets, and Crowdsourcing

       Bibliography

       Authors’ Biographies

       Acknowledgments

      This work was supported by funding from PHEME, DecarboNet, COMRADES, uComp, and the Engineering and Physical Sciences Research Council (grant EP/I004327/1). The authors also wish to thank colleagues from the GATE team, listed here in alphabetical order: Hamish Cunningham, Leon Derczynski, Genevieve Gorrell, Mark Greenwood, Johann Petrak, Angus Roberts, Ian Roberts, Dominic Rout, Wim Peters; and the many other colleagues who have contributed fruitful discussions.

      Diana Maynard, Kalina Bontcheva, and Isabelle Augenstein

      November 2016

      CHAPTER 1

       Introduction

      Natural Language Processing (NLP) is the automatic processing of text written in natural (human) languages (English, French, Chinese, etc.), as opposed to artificial languages such as programming languages, to try to “understand” it. It is also known as Computational Linguistics (CL) or Natural Language Engineering (NLE). NLP encompasses a wide range of tasks, from low-level tasks, such as segmenting text into sentences and words, to high-level complex applications such as semantic annotation and opinion mining. The Semantic Web is about adding semantics, i.e., meaning, to data on the Web, so that web pages can be processed and manipulated by machines more easily. One central aspect of the idea is that resources are described using unique identifiers, called uniform resource identifiers (URIs). Resources can be entities, such as “Barack Obama,” concepts such as “Politician” or relations describing how entities relate to one another, such as “spouse-of.” NLP techniques provide a way to enhance web data with semantics, for example by automatically adding information about entities and relations and by understanding which real-world entities are referenced so that a URI can be assigned to each entity.

      The goal of this book is to introduce readers working with, or interested in, Semantic Web technologies, to the topic of NLP and its role and importance in the field of the Semantic Web. Although the field of NLP has existed long before the advent of the Semantic Web, it has only been in recent years that its importance here has really come to the fore, in particular as Semantic Web technologies move toward more application-oriented realizations. The purpose of this book is therefore to explain the role of NLP and to give readers some background understanding about some of the NLP tasks that are most important for Semantic Web applications, plus some guidance about