Information Extraction: Algorithms and Techniques

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Digital Humanities Lab: Practices

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

Mathematics of Data: Models and Learning

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

Entity Disambiguation

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

Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.

Taxonomy Induction: Learning Concepts and Relationships

Explores taxonomy induction, learning relationships and concepts from documents.

Projects Presentation & Logistics

Covers the presentation of 4 projects in the course and related logistics.

Semantic Web: Knowledge Graphs

Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.

Taxonomy Induction: Learning Hierarchical Structures

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