Identity of long-tail entities in text /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ilievski, Filip, 1991- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : IOS Press, 2019.
Colección:Studies on the Semantic Web ; v. 043.
Temas:
Natural language processing (Computer science)
Natural Language Processing
Traitement automatique des langues naturelles.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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