| Description |
1 online resource |
| Series |
Studies on the semantic web ; volume 043 |
|
Studies on the Semantic Web ; v. 043.
|
| Contents |
Intro -- Title Page -- Contents -- Acronyms -- 1 Introduction -- 1.1 Background: Identity in the digital era -- 1.2 Challenge: Entity Linking in the long tail -- 1.3 Research questions -- 1.4 Approach and structure of the thesis -- 1.4.1 Describing and observing the head and the tail -- 1.4.2 Analyzing the evaluation bias on the long tail -- 1.4.3 Improving the evaluation bias on the long tail -- 1.4.4 Enabling access to knowledge about long-tail entities beyond DBpedia -- 1.4.5 The role of knowledge in establishing identity of long-tail entities -- 1.5 Summary of findings |
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1.6 Software and data -- 2 Describing and Observing the Head and the Tail of Entity Linking -- 2.1 Introduction -- 2.2 Related work -- 2.3 Approach -- 2.3.1 The head-tail phenomena of the entity linking task -- 2.3.2 Hypotheses on the head-tail phenomena of the entity linking task -- 2.3.3 Datasets and systems -- 2.3.4 Evaluation -- 2.4 Analysis of data properties -- 2.4.1 Frequency distribution of forms and instances in datasets -- 2.4.2 PageRank distribution of instances in datasets -- 2.4.3 Ambiguity distribution of forms -- 2.4.4 Variance distribution of instances |
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2.4.5 Interaction between frequency, PageRank, and ambiguity/variance -- 2.4.6 Frequency distribution for a single form or an instance -- 2.5 Analysis of system performance and data properties -- 2.5.1 Correlating system performance with form ambiguity -- 2.5.2 Correlating system performance with form frequency, instance frequency, and PageRank -- 2.5.3 Correlating system performance with ambiguity and frequency of forms jointly -- 2.5.4 Correlating system performance with frequency of instances for ambiguous forms -- 2.6 Summary of findings -- 2.7 Recommended actions -- 2.8 Conclusions |
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3 Analyzing the Evaluation bias on the Long Tail of Disambiguation & Reference -- 3.1 Introduction -- 3.2 Temporal aspect of the disambiguation task -- 3.3 Related work -- 3.4 Preliminary study of EL evaluation datasets -- 3.4.1 Datasets -- 3.4.2 Dataset characteristics -- 3.4.3 Distributions of instances and surface forms -- 3.4.4 Discussion and roadmap -- 3.5 Semiotic generation and context model -- 3.6 Methodology -- 3.6.1 Metrics -- 3.6.2 Tasks -- 3.6.3 Datasets -- 3.7 Analysis -- 3.8 Proposal for improving evaluation -- 3.9 Conclusions |
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4 Improving the Evaluation bias on the Long Tail of Disambiguation & Reference -- 4.1 Introduction -- 4.2 Motivation & target communities -- 4.2.1 Disambiguation & reference -- 4.2.2 Reading Comprehension & Question Answering -- 4.2.3 Moving away from semantic overfitting -- 4.3 Task requirements -- 4.4 Methods for creating an event-based task -- 4.4.1 State of text-to-data datasets -- 4.4.2 From data to text -- 4.5 Data & resources -- 4.5.1 Structured data -- 4.5.2 Example document -- 4.5.3 Licensing & availability -- 4.6 Task design -- 4.6.1 Subtasks -- 4.6.2 Question template |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (IOS Press, viewed January 14, 2020) |
| Subject |
Natural language processing (Computer science)
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|
Natural Language Processing
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Natural language processing (Computer science)
|
| Form |
Electronic book
|
| ISBN |
9781643680439 |
|
1643680439 |
|
