Concept

WordNet

Related lectures (15)

Explores lexical semantics, word sense, semantic relations, and WordNet, highlighting applications in language engineering and information retrieval.

Taxonomy Induction: Learning Concepts and Relationships

Explores taxonomy induction, learning relationships and concepts from documents.

Taxonomy Induction: Learning Hierarchical Structures

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

Information Extraction: Methods and Applications

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

Knowledge Representation: Semantics and Data Structures

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Semantic Web & Information Extraction

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

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Semantic Web Resources

Explores popular ontologies and knowledge bases like WordNet, WikiData, Google Knowledge Graph, and Schema.org, as well as Linked Open Data sets.

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

NLP Pre-processing: Tokenization, Stop Words, Lemmatization

Covers tokenization, stop words removal, and lemmatization for NLP tasks.

Probabilistic Retrieval

Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.

Neural Word Embeddings: Learning Representations for Natural Language

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

Neural Word Embeddings

Introduces neural word embeddings and dense vector representations for natural language processing.

Probabilistic Retrieval: Practical Relevance Feedback

Explores practical relevance feedback in probabilistic retrieval and query optimization.

Semantic Web: Knowledge Representation

Explores Semantic Web concepts, RDF syntax, and knowledge representation using RDF statements and Schema.org.