[MXNLP-1-01] Neural Probabilistic Language Model (NPLM) - [1]

*** Dubbing: [ English ] [ 한국어 ]
In this series, we'll explore word embeddings in Natural Language Processing.

In machine learning, embeddings refer to the process of representing complex, high-dimensional data, such as words, images, or audio, as numerical vectors in a continuous, lower-dimensional vector space. Also in Natural Language Processing, embeddings refer to representing natural language used by humans as real-valued vectors that can be understood by machines.

Word embeddings are numerical representations of words designed to capture their meanings and relationships in a continuous vector space. Each word is mapped to a dense vector where the distance and direction between vectors reflect semantic similarities.

In this video, we'll explore the Neural Probabilistic Language Model (NPLM), an early concept in word embeddings. We will briefly review the NPLM paper, and analyze the neural architecture of NPLM in detail.

The NPLM was introduced by Yoshua Bengio, et. al, in their 2003 paper titled "A Neural Probabilistic Language Model".

#NeuralProbabilisticLanguageModel #NPLM #WordEmbeddings #DistributedRepresentation