Information Extraction & Knowledge Inference

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Related lectures (32)

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Entity & Information Extraction

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

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.

Text Understanding

Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.

Knowledge Inference for Graphs

Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.

Matrix Factorization: Linking Text to Knowledge Bases

Explores distant supervision for linking text to knowledge bases using entity extraction and classifiers.

Expert Systems: Backward Chaining

Explores expert systems, backward chaining, and uncertainty through fuzzy logic in practical applications.

Entity & Information Extraction

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

Semantic Web: Modeling and Ontologies

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

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.

Mathematical Analysis: Functions and Composition

Covers the analysis of functions, composition, and mathematical induction.