Taxonomy Induction: Learning Concepts and Relationships

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Related lectures (32)

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Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Taxonomy Induction: Learning Hierarchical Structures

Explores taxonomy induction, learning terms and relationships to construct hierarchical structures.

Semantic Web & Information Extraction

Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.

Information Extraction & Knowledge Inference

Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.

Information Extraction: Algorithms and Techniques

Explores algorithms and techniques for information extraction, including Viterbi algorithm, named entities recognition, and distant supervision.

Entity & Information Extraction

Explores knowledge extraction from text, covering key concepts like keyphrase extraction and named entity recognition.

Entity & Information Extraction

Explores information extraction using classifiers, features, and syntactic analysis.

Digital Humanities Lab: Practices

Introduces students to digital humanities through practical exercises, such as working on a large ongoing project.

Information Extraction: Approaches and Techniques

Covers Information Extraction approaches, including hand-written patterns and supervised learning.

Semantic Web: Modeling and Ontologies

Explores the Semantic Web, database schemas, XML data model, and ontologies.