Information Extraction: Methods and Applications

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Related lectures (29)

Page 2 of 3

Explores Information Extraction approaches like hand-written patterns and distant supervision, with examples of entity pairs matching patterns.

Information Extraction: Approaches and Techniques

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

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.

Knowledge Inference: Entity Disambiguation and Graph Embeddings

Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

Knowledge Modeling: Introduction

Explores manual creation of knowledge bases and challenges in knowledge extraction from text.

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

Embedding Models: Concepts and Retrieval

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

Entity Disambiguation

Explores entity disambiguation techniques, including NER, Viterbi algorithm, and GPT models, emphasizing prompt design and in-context learning.

Knowledge Inference

Explores knowledge inference, embedding techniques, and schema matching in data integration.

Mathematics of Data: Models and Learning

Explores models, learning paradigms, and applications in Mathematics of Data.