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Robert Speer

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All ConceptNet (3)Machine Learning (2)Artificial intelligence (2)Data Normalization (1)No Data Left Behind (1)QC-Learning (1)Goal-Directed Learning (1)Open Mind Common Sense (1)Word Abstractions (1)Mapping Langauge (1)NLP (1)Word Vectors (1)

From “Recent Advances in Natural Language Processing V”

Each sentence of OMCS was entered by a goal-directed user hoping to contribute common sense, resulting in a wealth of statements that focus on simple, real-world concepts that often go unstated.

Open Mind Common Sense Goal-Directed Learning ConceptNet Artificial intelligence QC-Learning

From “Representing General Relational Knowledge in ConceptNet 5”

Normalization inherently involves discarding information, but since ConceptNet 3, we have ensured that this information is stored with the assertion and not truly discarded

ConceptNet Data Normalization Machine Learning No Data Left Behind

ConceptNet includes not just definitions and lexical relationships, but also the common-sense associations that ordinary people make among these concepts. Its sources range in formality from dictionaries to online games.

ConceptNet Mapping Langauge Machine Learning

From “Better, less-stereotyped word vectors”

Word embeddings or word vectors are a way for computers to understand what words mean in text written by people. The goal is to represent words as lists of numbers, where small changes to the numbers represent small changes to the meaning of the word. This is a technique that helps in building AI…

Word Vectors NLP Artificial intelligence Word Abstractions

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